This paper investigates five different GARCH models (GARCH, EGARCH, IGARCH, Component GARCH (CGARCH) and GJR-GARCH) along with six distributions (Normal, Student’s t, GED and their skewed forms), which are used to forecast the volatility for the Bulgarian Stock index SOFIX. We use a GARCH model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data covers the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at forecasting the volatility of SOFIX and which distribution is most appropriate. The results, based on BIC and AIC information criteria, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student t-distribution is preferred for forecasting. With the help of 5000 simulations, it is estimated that the chances of Index SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.

Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of energy sector worldwide late 1990s. Given these facts, this paper tries to come up with a precise and flexible forecasting model for the wholesale electricity price for the Italian power market on an hourly basis. We utilize artificial intelligence models such as neural networks and bagged regression trees that are rarely used to forecast electricity prices. After model calibration, our final model is bagged regression trees with exogenous variables. The selected model outperformed neural network and bagged regression with single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm some theoretical specifications of the model. As a policy implication, this model might be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process.

A new model for international bed numbers is applied to local area populations for English Clinical Commissioning Groups (CCGs) where the Age Standardized Mortality Rate (ASMR) is used as a measure of population health and hence for total occupied hospital bed demand (elective plus emergency admission types, including acute, rehabilitation, mental health, and maternity), rather than simple bed availability. A 100-unit increase in ASMR (European Standard population) leads to a 15.3 – 30.7 (feasible range) unit increase in occupied beds per 1,000 deaths. ASMR shows why the Australian states of the Northern Territory and Tasmania have an intrinsic higher bed demand than the other states – although in both cases the bed planning methodologies failed to indicate this need and both states show a large bed deficit. Due to the high volatility in bed demand the average bed demand for 2019 was determined by a linear trend using data for 2014/15 to 2019/20 — as the pre-COVID-19 baseline. The slope of the relationship between occupied beds per 1,000 deaths and deaths per 1,000 population shows a power law function. As is to be expected there is scatter around the trend line which will partly arise from uncertainty in the expected occupied beds in 2019, fluctuations in both ASMR and deaths per 1,000 population from one year to another, changes in the number of persons on the elective, outpatient and diagnostic waiting lists, and local area variation in births and consequently upon maternity, neonatal and pediatric bed demand. Additional variation in bed demand will arise from differences in the level of funding for social care, especially elderly care, and to a lesser extent upon patients using private health care, and charitable hospices for palliative care. However, after including the change in bed demand arising from ASMR the model is sufficiently accurate to identify gross differences in bed demand as a prelude to further investigation. The model requires further extension to cover specialty-level demand, and perhaps segregation between elective and emergency types of inpatient demand. A method for benchmarking the whole hospital’s average bed occupancy is given which enables them to run at optimum efficiency and safety. Using this method English hospitals can be shown to operate at highly disruptive and unsafe levels of bed occupancy leading to deleterious ‘turn-away’. Turn-away implies bed unavailability for the next arriving patient. The problems associated with crafting effective bed planning are illustrated using the English NHS as an example.

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which provides powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability.

The Eastern Kunlun Fault Zone (EKFZ) is located in a seismic blank area. This study investigates the hydrogeochemical characteristics of varied-temperature springs in the EKFZ to understand the relationship between fractures, fluids and earthquakes. It aids in establishing their induction mechanisms and assists in monitoring the earthquake precursors of the EKFZ. In this study, the main elements, trace elements, hydrogen and oxygen isotopes and strontium isotopes of 23 varied-temperature springs in the EKFZ were analyzed. The results reveal the source of groundwater recharge, hydrochemical types, mineral saturation, and for a select few springs indicative of geothermal activity, collected geothermal reservoir temperatures. The study also explores the relationship between earthquakes and springs, identifying sensitive chemical indicators. Based on continuous monitoring at Maqu Station, we found that before the occurrence of the Maerkang Ms 6.0 earthquake, the concentration of Cl− rose by 2.9 times, and 46 days before the Gande earthquake (Ms = 4.7), the concentrations of Na+ and SO42− significantly increased. Finally, a conceptual model of the hydrogeochemistry of springs is presented to describe the groundwater circulation in the study area. This research is of great significance for the analysis of fluid geochemistry in this area and for assessing the seismic hazards of the EKFZ.

When dealing with limited sample sizes or missing values in time series data, achieving high accuracy in forecasting becomes challenging. This challenge is particularly common in wind energy forecasting, where many wind farms lack complete data. In this paper, we employ the widely-used ARIMA (Autoregressive Integrated Moving Average) model for time series forecasting. To address the difficulties posed by limited sample sizes and missing values, we adopt a unique approach by utilizing multiple ARIMA models for different datasets. A key distinction from other multiple models methods for time series forecasting lies in each model within our approach using its own dataset, making the fusion process more intricate. To overcome this, we incorporate neural networks and two techniques for model fusion: direct fusion with a learning mechanism and compensation for missing data through training. For each ARIMA model, we introduce a noise estimation method to circumvent the white noise assumptions inherent in ARIMA models. Finally, we apply these methods to wind energy prediction, and our experimental results showcase a significant enhancement in forecasting accuracy.

The accurate forecasting of solar power output is essential for ensuring the stable and efficient operation of grid-connected photovoltaic (PV) systems, especially when faced with variable weather patterns. This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Leveraging machine learning algorithms, including XGBoost regression and neural networks, our model aims to identify complex patterns and relationships within the datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. The performance of the forecasting model will be evaluated using error metrics like mean absolute error (MAE) and root mean squared error (RMSE). This study leverages a comprehensive dataset from Kaggle and utilizes advanced machine learning techniques to generate valuable insights. These insights can inform the development of sustainable energy policies and practices, ultimately leading to a more efficient and reliable renewable energy infrastructure.

This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS to read and analyze the dataset, incorporating statistical measures like mean, median, and standard deviation to understand the dataset. The CNN model was trained using the ReLU activation function and mean absolute error as the loss function. Model performance was evaluated through MAPE graphs, comparing the mean absolute percentage error (MAPE) between training and validation sets and between true and predicted emissions, and analyzing trends in yearly greenhouse gas emissions. The results demonstrated that the CNN model achieved a high level of accuracy in predicting methane emissions, with a low MAPE between the expected and actual values. This approach should enhance our understanding of methane emissions from Sargassum, contributing to more accurate environmental impact assessments and effective mitigation strategies.

In this paper, we study the usage of machine learning models for sales time series forecasting. The effect of machine learning generalization has been considered. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking technics, we can improve the performance of predictive models for sales time series forecasting.

The pollution of the bays in Abidjan is a major concern for the Ivorian policy makers. In fact, the pollution of the bays induce high costs to the society while impacting population health dramatically. As a result, pollution reduction management of production activities has been undertaken in the Cocody Bay area. To our knowledge, no study has yet proposed a model to evaluate the cost-effectiveness of these pollution management strategies. A cost-effectiveness model, based on Monte Carlo simulation, was developed to assess the economic and environmental impacts of various scenarios characterized by a set of production practices, both in the short term and in the long term. The authors discuss the steps and input parameters of the model presented. The proposed model may serve as the basis for identifying an optimal production scenario defined as the scenario with the best incremental cost-effectiveness ratio considering a willingness to pay (WTP) threshold. The WTP, to be estimated based on the gross domestic product of Côte d’Ivoire, represents the opportunity costs associated with selecting the optimal scenario. The current framework can also be applied to other settings facing similar challenge.

This study involves the development of a hybrid forecasting framework that integrates two different models in a framework to improve prediction capability. Although the concept of hybridization is not a new issue in forecasting, our approach presents a new structure that combines two standard simple forecasting models uniquely for superior performance. Hybridization is significant for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. The proposed architecture includes serially connected ARIMA and ANN models. The original data set is first processed by ARIMA. The error (i.e., residuals) of the ARIMA is sent to the ANN for secondary processing. Between these two models, there is a classification mechanism where the raw output of the ARIMA is categorized into three groups before they are sent to the secondary model. The algorithm is tested on well-known forecasting cases from the literature. The proposed model performs better than existing methods in most cases.

Corporate Diagnosis is now recognized as an important tool by decision makers to predict and correct burgeoning problems that a corporation may face. Methods based on this model stem from the use of mathematics and are increasingly being applied in the analysis of production processes. The goal of this paper is to use a logistic regression to design a scoring model for non-financial corporations in industry. Based on the data obtained from the Registry of the Slovak Republic for 738 non-financial corporations, according to SK NACE 26, SK NACE 27, the proportional financial metrics, using the logistic regression method, were calculated. By applying these methods, two logistic regression models were found to reliably estimate the probability of bankruptcy for a firm.

One of the most important sources of energy is the sun. Taiwan is located at north 22-25° latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location which can obtain sustainable and stable solar resources. This study takes research on the forecast of solar radiation to maximize the benefits of solar power generation, and develops methods that can predict the future solar radiation pattern to help reduce the costs of solar power generation. This study builds supervised machine learning models, known as deep neural network (DNN) and long short-term memory neural network (LSTM). The hybrid supervised and unsupervised model, namely cluster-based artificial neural network (k-means clustering and fuzzy C-means clustering-based models), was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time updated solar radiation forecast system capable of predicting the next 12 hours. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models have their advantages and disadvantages in predicting different future times. Therefore, the hybrid prediction system can predict future solar radiation more accurately than a single model.

An important indicator of human-related pollution in watersheds is dissolved oxygen (DO). The DO is highly dependent on both space and time characteristics of the watershed and is directly linked to eutrophication, which impairs the development of both the aquatic fauna and flora, also negatively impacting the water quality. Aspiring to reach a more accurate and precise forecasting approach to predict levels of DO, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated for the Credit River Watershed, and the results were compared with both benchmarking and literature-found approaches. The proposed Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and Root Mean Squared Error (RMSE) values of 97% and 0.34 ppm, respectively. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results, and that temperature is a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model stands as a state-of-the-art solution for DO forecasting, with potential for real-time water quality applications.

Buildings have started to play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large scale energy management strategies from the supply side to the consumer side. When the buildings integrate local renewable energy generation in the form of renewable energy resources they become prosumers and this reflects into additional complexity into the operation of the interconnected complex energy systems. A class of methods of modelling the energy consumption patterns of the building have recently emerged as black-box input-output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produces by non-deterministic processes underlying the energy consumption. We present an application of a class of neural networks, namely deep learning techniques for time series sequence modelling with the goal of accurate and reliable building energy load forecasting. The Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects and are considered suitable for further used in future in situ energy management at the building and neighbourhood levels.

The age-related loss of muscle mass and strength is referred to as “sarcopenia”. The combined effect of sarcopenia and osteoporosis is a devastating threat to the old adult, accounting for reduced mobility, risk for falls and subsequent fractures. In previous studies, we observed a high degree of inter-individual variability of paraspinal muscle fatty infiltration, one most relevant index of muscle wasting. This aspect solicited us to develop a computerized method to quantitatively characterize muscle fatty infiltration in aging and diseases. Magnetic resonance images of paraspinal muscles from 58 women of different age (age range 23-85 years) and physio-pathological status (healthy young, pre-menopause, post-menopause, and os-teoporosis), were used to set up the method. It is based on fractal analysis of lean muscle area (contractile muscle) to estimate muscle fatty infiltration. In particular, lacunarity was computed by parameter β from our hyperbola model function, used to fit the GBA (gliding box algorithm) curvilinear plot. Succolarity was estimated by parameter µ, for the four main directions, through an algorithm implemented on purpose. Results show that fractal lacunarity, by quantifying muscle fatty infiltration, can discriminate be-tween osteoporosis and healthy aging while succolarity can separate the other three groups showing similar lacunarity. Therefore, fractal features of contractile muscle, by measuring fatty infiltration, can represent good indices of sarcopenia in aging and disease.

In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-Wavelet-ANN models are compared with those obtained from Wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid Wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the Wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-Wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicators in rivers.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik (IS), and Tromso (NO). Our framework leverages historical ground measures and 24-hour predictions from 9 models provided by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM predictions for the following 24 hours. Furthermore, we compare the performance of various memory cells based on artificial neural networks (ANN), including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), echo state networks (ESNs), and windowed multi-layer perceptrons (MLPs), which are commonly employed in time series forecasting tasks. Irrespective of the chosen memory cell type, our results demonstrate that the proposed framework consistently outperforms the CAM models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Additionally, we investigate the impact of outliers on the overall model performance.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to forecast energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the forecasting ability of the FGM(1,1) with traditional ones, like standard GM(1,1) and ARIMA(1,1,1) models. Also, it illustrates the view of BRICS's nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM(1,1) for a specific range of order parameters and the ARIMA(1,1,1) model and the usefulness of both approaches for energy consumption efficient forecasting.

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, leading to increased economic and social benefits. Currently, some simple AI and hybrid models has issues to deal with and struggle with multivariate dependencies, long-term dependencies, and non-linear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while Lasso regression serves as the meta-learner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in forecasting accuracy, with a substantial reduction in Mean Absolute Percentage Error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, suggesting potential operational efficiency benefits for the power industry.

Accurately predicting the power of solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for the balanced operation and optimized dispatch of the power grid system, and reduces operating costs. Solar PV power generation depends on weather conditions, which are prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility and intermittency. Recently, the demand for further investigation and effective use on the uncertainty of short-term solar PV power generation prediction has been getting increasing attention in many application of renewable energy sources. In order to improve the predictive accuracy of output power of solar PV power generation and develop a precise predictive model, the authors worked predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is one of the important aspects for optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods in the reviewed literature are classified according to the input data source used for accurate predictive models, and the case studies and examples proposed are analyzed in detail. The contributions, advantages and disadvantages of the predictive probabilistic methods are compared. Finally, the future studies of short-term solar PV power forecasting is proposed.

The development of plant-based meat analogues has become a significant challenge for the food industry in recent years due to the increasing demand for sustainable and healthier proteins in the context of a global protein transition. Plant-based meat analogues imitate the visual, textural, and chemical properties of traditional meat products and are required to closely resemble meat to appeal to consumers. In addition, consumers demand natural, clean-label, and nutritional and healthy products. To address these challenges, the food industry must develop highly healthy, nutritious, and E-number-free meat analogue products. Understanding the functionality of each ingredient and its role in the food matrix is crucial to being a key player in the innovation of the meat analogues market. This review provides updated information on the primary ingredients utilized for the development of plant-based burger meat alternatives and their functionality. The key components of meat analogue burgers are outlined, including plant proteins, binding agents, fats and oils, flavorings, colorings, preservatives, fortificants, and clean-label considerations.

Keywords: Artificial Neural Network (ANN), Support Vector Machine (SVM), Support Vector Regression (SVR), Lightweight Gradient Boosting Machines (Light GBM), Machine Learning, Solar Irradiance (SI), Solar forecasting

To improve the monitoring of the electrical power grid it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface, and to evaluate the supportability of these components. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. Choosing which method to use is always a difficult task since some models may have higher computational effort. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. A review and comparison of these well-established methods for time series forecasting is performed. From the results of the best structure of the model, the hyperparameters are evaluated and the Wavelet transform is used to obtain an enhanced model.

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Solar energy adoption worldwide has expanded exponentially due to a surge in international interest in producing clean energy and the declining cost of solar power plants and their technology. It is anticipated that by 2050, solar will have surpassed fossil fuels to become the primary source of energy. However, one of the main challenges associated with solar energy production is the instability of photovoltaic (PV) power generation because of weather changes. Short-term forecasting of the power output of photovoltaic systems is essential for efficient management of the power grid and energy markets. This paper aims to evaluate the ability of deep learning (DL) models to provide accurate forecasting of hourly global horizontal irradiance (GHI) using different sets of features, including weather and aerosol variables along with solar radiation components. The results show that the best forecast skills are achieved by the long short-term memory autoencoder (LSTM-AE) model.

The performance of 5 forecasting models was investigated for predicting nursery mortality using the master table built for 3,242 groups of pigs (~ 13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model’s performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE=0.406), Mean Absolute Error (MAE=0.284), and Coefficient of Determination (R2=0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2=0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (5>%) or low (5<%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking conditions variables collected post-placement in nursery sites.

It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real time through an advanced metering infrastructure (AMI) sensor, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include the autoregressive integrated moving average, radial basis function-artificial neural network, quantitative multi-model predictor plus, and long short-term memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand by applying the data on the amount of water consumption by purpose and the pipe diameter of an end-use level of the SWG demonstration plant in each demand forecasting model. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from the AMI, and the performance of each model was assessed. The Smart Water Grid Research Group installed ultrasonic-wave-type AMI sensors in the block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the residual, root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), and Pearson correlation coefficient (PCC) as indices. The water demand forecast was slightly underestimated in models that employed the assessment results based on the RMSE and NRMSE. Furthermore, the forecasting accuracy was low for the NSE due to a large number of negative values; the correlation between the observed and forecasted values of the PCC was not high, and it was difficult to forecast the peak amount of water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

Accurate measurement of systemic financial risk is critical to maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was con-structed by integrating six dimensions and employing Gray Relation Analysis (GRA) for dimensionality reduction of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA) optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trend of financial pressure indicators, the relationship between financial pressure and economic activity can be effectively identified. The research findings indicate that: (1) The CFSI is capable of accurately reflecting the current financial stress situation in China; (2) The IGSA-RBFNN demonstrates good robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology benefits policymakers and regulatory authorities to formulate preventive measures and risk management by identifying potential risks and vulnerabilities in advance.

To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

Logistics plays an important role in globalized companies and contributes to the development of foreign trade. A large number of external conditions, such as recession and inflation, affect logistics. Therefore, managers should find ways to improve operational performance, enabling them to increase efficiency while considering environmental sustainability due to the industry’s large scale of energy consumption. Based on data collected from the financial reports of top global logistics companies, this study uses a DEA model to calculate corporate efficiency by implementing a Grey forecasting approach to forecast future sustainability values. Consequently, the study addresses the problem of how to enhance operational performance while accounting for the impact of external conditions. This research can help logistics companies develop operation strategies in the future that will enhance their competitiveness vis-à-vis rivals in a time of global economic volatility.

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, support quantative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation and multi-year daily use for public health and clinical support decision-making of a particle filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian Sequential Monte Carlo algorithm of Particle Filtering allows the model to be re-grounded daily and adapt to new trends within daily incoming data – including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of dynamic model state, support probabilistic model projection of epidemiology and health system capacity utilization and service demand and probabilistically evaluate trade-offs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision making across Canada, such methods offer strong support for evidence-based public health decision making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

In financial time series modelling and forecasting, combining ARMA and GARCH models tend to produce superior and reliable models for volatility persistence, half-life volatility and backtesting (application of model in real life). In Nigeria, banking stocks are mostly traded because of its potential benefits to investors. This study modelled and forecasted the Guaranty Trust (GT) Bank daily stock returns from January 2^nd 2001 to May 8^th 2017 data set collected from a secondary source. The ARMA-GARCH models, persistence, half-life and backtesting were used to analysed the collected data using student t and skewed student t distributions, and the analyses are carried out R environment using rugard and performanceAnaytics Packages. The study revealed that using the lowest information criteria values only could be misleading rather we added the use of backtesing. The ARMA(1,1)-GARCH(1,1) models fitted exhibited high persistency in the daily stock returns while the days it takes for mean-reverting of the models ranges from 5 days to 100 days but unfortunately the models failed backtesting. The results further revealed ARMA(1,1)-eGARCH (2,2) model with student t distribution provides a suitable model for evaluating the GT bank stock returns among the competing models while it takes less than 30 days for the persistence volatility to return back to its average value of the stock returns. This study recommended that researchers should adopt backtesting approach while fitting GARCH models while GT bank stocks investor should be assured that no matter the fluctuations in the stock market, the GT bank stock returns has the ability to returns to its mean price return.

The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

With the global objectives of achieving a ‘carbon peak’ and ‘carbon neutrality’, along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from January 1st to December 30th in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

One of the primary causes of the mechanical seal failure is rotor vibration. Traditional dynamic seal models often would not be able to fully explaining the failure mechanisms. The seal dynamic models proposed in this paper, including those developed by the authors, prove valuable in predicting seal dynamics during operation in specific turbomachinery and in explaining the causes of seal failure. The single-mass dynamic model is suitable for analyzing contact mechanical seal and simple design dry gas seals dynamics. The two-mass dynamic model is used for investigating operational dynamics processes in classical dry gas seals under complex loading conditions. The three-mass dynamic model is used to study various complex mechanical seal types. This model is able to determine the normal operating condition range and to explain leakage mechanisms in the presence of excessive rotor vibrations.

: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a hybrid model based on the LSSVM optimized by the IBFOA for forecasting the daily electricity load in Peninsular Malaysia. The IBFOA for parameter optimization of LSSVM is introduced. The IBFOA based on the sine cosine equation is proposed to adjust the constant step size in the BFOA, which creates an imbalance between the exploration and exploitation during optimization. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using MAPE as the objective function. Comparative analysis demonstrates the model, achieving the highest R2 (0.9880) and significantly reducing error metrics (MAPE, MAE, RMSE, MSE, NRMSE) compared to the baseline LSSVM (average reduction of 27.72% to 47.72%). Additionally, IBFOA exhibits faster convergence and higher accuracy compared to BFOA, highlighting the accuracy of LSSVM-IBFOA for short-term load forecasting.

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. Due to the technological and econonomic limitations of energy storage solutions, using other, mosty conventional, sources to cover energy shortfalls and at the same time utilising solar surpluses production becomes necessary. The need to optimize energy systems, ensuring power continuity, and balancing energy supply and demand has led to the development of various forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasizes the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses the emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. This includes defining research questions, selecting eligibility criteria, literature search, data extraction, synthesis, and assessing bias and quality. The article contributes to the solar forecasting field by providing up-to-date knowledge, along with insights on the emerging trends, future research directions, and anticipating implications for the theory and practicce.

The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers calculation. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer's risk. If a product doesn't meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended, and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with an opening upwards. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties are determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model's performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest.

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction.Experiments are conducted on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our model can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores above 96\% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC.

With the development of organizational biology and forced from the EU Cosmetic Regulation (EU/1223/2009) with the complete ban of animal testing for cosmetic purposes, 3D skin models have been widely developed due to their similar physiological structure and metabolic function to native human tissue. 3D skin models for in vitro cosmetic evaluation are still promising alternative methods for evaluating phototoxicity, corrosivity and irritation. These models have been used for early screening and later validation of toxicity and efficacy evaluation by many well-known cosmetic companies. 3D skin models have become an important tool in cosmetic evaluation research. This article reviews the development of 3D skin models and their application in cosmetic evaluation. An outlook on the current application and future technological development directions in the cosmetic field is also presented.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

The goal of VAR or BVAR is the characterization of the dynamics and endogenous relationships among time series. Also the VAR models are known for their applications to forecasting and policy analysis. This paper compare the performance of VAR and Sims-Zha Bayesian VAR models when the multiple time series are jointly influenced by different levels of collinearity and autocorrelation in the short term (T=16, 32, 64 and 128). Five levels (-0.9,-0.5, 0,+0.5,+0.9) of collinearity and autocorrelation were considered and the results from the simulation study revealed that VAR(2) model dominated for no and moderate levels of autocorrelation (-0.5, 0, +0.5) irrespective of the collinearity level except in few cases when T=16. While the BVAR models dominated for high autocorrelation levels (-0.9 and +0.9) irrespective of the collinearity level except in few cases when T=128. The performance of the models varies at different levels of the collinearity and autocorrelated error, and also varies with the short term periods. Furthermore, the values of the RMSE and MAE criteria decrease as a result of increase in the time series length. In conclusion, the performance of the forecasting models depend on the time series data structure and the time series length. It is therefore recommended that the data structure and series length should be considered in using an appropriate model for forecasting.

Persistent land subsidence or uplift can cause serious latent hazards to urban infrastructure. To thoroughly investigate the causes and impacts of land surface deformation, this study examines the case of Wuxi City, focusing on the land fluctuation patterns in the region following the enforcement of the groundwater abstraction prohibition policy in 2005. This research utilizes the PS-InSAR technique to quantify land surface deformations in the Wuxi region from November 2015 to June 2023 and analyzes 100 SAR images to obtain comprehensive spatiotemporal evolution data of the land surface. Furthermore, this study conducts an in-depth analysis of the effects of various factors, including groundwater, precipitation, and soil geology, on the characteristics of land surface deformation. It pioneers the application of the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model to analyze the time-series displacement in the study area and innovatively introduces the "amplitude factor" index to assess land surface fluctuations. This study investigates the recent land surface deformation patterns and mechanisms in Wuxi City. By integrating the "amplitude factor", it provides a scientific foundation for future urban development and disaster mitigation strategies in Wuxi City, offering valuable insights for related research.

Sentiment analysis to characterize properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy transform to capture stylized facts and mutual connections between time series having different nature. Six years of daily Bitcoin Prices and Google Trends are analyzed in order to establish new perspectives in the management of their dynamics.

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. By leveraging computer vision technologies to extract key traffic parameters from video streams, the approach demonstrated a notable improvement in aligning the generated data from the calibrated simulation model with car sensor data, achieving an average improvement of over 50% compared to the uncalibrated macroscopic model. Moreover, there was a substantial reduction in data drift for the machine learning model integrated into the virtual transport space using vehicle-to-everything technology, resulting in a more than fourfold decrease in the average absolute error of the model.

The rapid advancement of large language models (LLMs), such as ChatGPT, has revolutionized natural language processing, enabling these models to generate coherent and contextually relevant text. These capabilities hold significant potential for various applications, particularly in the medical field. LLMs can assist healthcare professionals by providing accurate information, supporting diagnostic processes, and enhancing medical education. However, the implementation of LLMs in clinical settings faces several barriers, including technical limitations, ethical concerns, regulatory constraints, and practical challenges. Ensuring accuracy, reliability, and interpretability is crucial, along with addressing biases, ensuring data privacy, and establishing ethical guidelines. Future research should focus on improving training techniques, developing explainable AI methods, and creating efficient, resource-saving models. Expanding applications through domain-specific models and multi-modal integration can further unlock the potential of LLMs. By addressing these challenges and prioritizing responsible development, LLM technology can be safely and effectively integrated into various domains, driving innovation and improving outcomes. This paper explores the current state of LLM technology, its medical applications, barriers to implementation, and directions for future research and development

Drug-induced liver injury (DILI) stands as a significant cause of drug attrition. To mitigate clinical DILI risks, assessing drugs using human liver models is crucial since animal studies may fall short due to species-specific liver pathway variations. Cell-based preclinical hepatotoxicity testing is often pertinent. In the present study, cells from a human liver cancer line (HepG2, HepaRG) were cultured in both formats of 2D and 3D spheroids to explore their responses to drugs. Liver-specific marker expressions across cell lines and culture formats were also examined to assess disparities in drug-induced liver injury marker expressions. In cytotoxicity assays, HepG2 cells exhibited heightened sensitivity to amiodarone HCl, while HepaRG cells showed greater sensitivity to acetaminophen. Generally, 2D cultures were more responsive to drugs than 3D cultures. Consistency was observed between cytotoxicity assays and liver injury markers AST and ALT. Variations were also noted in liver cell marker secretion, with albumin and urea concentrations declining post-drug treatment, particularly with higher baseline expression in HepaRG cells and 3D cultures. Additionally, mRNA expression levels of CYP enzymes involved in hepatocellular drug metabolism were compared following treatment with enzyme inducers. CYP1A2 and CYP2C9 were not expressed in HepG2 cells. On the other hand, CYP3A4 was expressed higher in 3D culture than in 2D culture. HepaRG cells exhibited significantly increased expression of all three enzymes post-treatment. Notably, enzyme expression was notably higher in 3D cultures than in 2D cultures. Collectively, these findings suggest that HepaRG cells and 3D cultures hold promise for evaluating DILI during early-stage drug development.

Especially for drought periods, the higher the accuracy of reservoir inflow forecasting, the more reliable the water supply from a dam. The article focuses on probabilistic forecasting of seasonal inflow to reservoirs and determines estimates from the probabilistic seasonal inflow according to drought forecast results. The probabilistic seasonal inflow was forecasted by a copula-based Bayesian network employing a Gaussian copula function. Drought forecasting was performed by calculation of the standardized streamflow index value. The calendar year is divided into four seasons; the total inflow volume of water to a reservoir for a season is referred to as the seasonal inflow. Seasonal inflow forecasting curves conforming to drought stages produce estimates of probabilistic seasonal inflow according to the drought forecast results. The forecasted estimates of seasonal inflow were calculated by using the inflow records of Soyanggang and Andong dams in the Republic of Korea. Under the threshold probability of drought occurrence ranging from 50 to 55 %, the forecasted seasonal inflows reasonably matched critical drought records. Combining the drought forecasting with the seasonal inflow forecasting may produce reasonable estimates of drought inflow from the probabilistic forecasting of seasonal inflow to a reservoir.

Variable-Angle Tow (VAT) laminates can improve straight fiber composites mechanical properties thanks to the application of curvilinear fibers. This characteristic allows to achieve ambitious objectives for design and performance purposes. Nevertheless, the wider design space and the higher number of parameters result in a more complex structural problem. Among the various approaches that have been used for VATs study, Carrera’s Unified Formulation (CUF) allows to obtain multiple theories within the same framework, guaranteeing a good compromise between the results accuracy and the computational cost. In this article, the linear buckling behavior of VAT laminates is analyzed through the extension of CUF 2D plate models within the Reissner’s Mixed Variational Theorem (RMVT). Results show that RMVT can better approximate the prebuckling non-uniform stress field of the plate when compared to standard approaches, thus improving the prediction of the linear buckling loads of VAT composites.

This paper presents the methods of estimating the mean square error of hydrological forecasts, allowing for assessment of their practical applicability. Depending upon the amount and composition of available hydrometeorological data, an appropriate method for forecast error estimation is chosen. A system of statistical tests for comparison of different forecasting methods for the same hydrologic characteristic with the same lead time is presented. These tests allow for choosing an optimal and most accurate forecasting method. Hydrological forecasting method efficiency estimation is based on comparing the forecast error with climatology or inertial (persistence) forecast error using presented tests.

Hybrid forecasting methods have emerged as a solution surpassing the limitations of both statistical and deep learning approaches. While the first emphasize the significance of variables, they often produce worse forecasting results when compared to newer techniques. In contrast, deep learning models remain enigmatic "black boxes" in terms of interpretability, although achieving better results in forecasting. This article introduces the Comprehensive Cross-Correlation and Lagged Linear Regression Deep Learning (CCLR-DL) framework, designed to harness the best of both approaches, enhancing forecasting accuracy while retaining model interpretability through a feature selection process. CCLR-DL blens cross-correlation analysis, lagged multiple linear regression and granger's causality procedures with deep learning architectures based on LSTM. In a practical demonstration, CCLR-DL was applied to a real database of clinical visits associated to diagnoses in Catalonia, Spain (tracking a population of 6.3 million patients during 10 years). Predicting visits enables the healthcare managers to be ready for future demand shifts. Results demonstrate a consistent and substantial improvement over standalone statistical and deep learning methods when predicting healthcare demand. This hybrid approach not only showcases its efficacy but also offers a promising solution to the challenge of balancing predictive accuracy with model explicability. In this context, this work aims to design and validate a method for feature selection and forecasting of multivariate high dimensional time series datasets not only to improve prediction accuracy but also to model transparency by identifying a subset of variables that improve predictions and G-cause the target variable.

Predicting human trajectories poses a significant challenge due to the complex interplay of pedestrian behavior, which is influenced by environmental layout and interpersonal dynamics. This complexity is further compounded by variations in scene density. To address this, we introduce a novel dataset from the Festival of Lights in Lyon 2022, characterized by a wide range of densities (0.2-2.2 ped/m2). Our analysis demonstrates that density-based classification of data can significantly enhance the accuracy of predictive algorithms. We propose an innovative two-stage processing approach, surpassing current state-of-the-art methods in performance. Additionally, we utilize a collision-based error metric to better account for collisions in trajectory predictions. Our findings indicate that the effectiveness of this error metric is density-dependent, offering prediction insights. This study not only advances our understanding of human trajectory prediction in dense environments but also presents a methodological framework for integrating density considerations into predictive modeling, thereby improving algorithmic performance and collision avoidance.

The extent of correlation or co-movement among the returns of developed and/or emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, we analyzed the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners. The Dynamic Conditional Correlation - Exponential Generalized Autoregressive Conditional Heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibit time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises.

The multiphase motors due to their reliability find new areas of application in the industry and transportation. Developing of any system with multiphase motors at first requires elaborate model of the system, define control mode and controllers. The model of control system for the six-phase drive is based on its conventional d-q mathematical model and indirect field-oriented control. Simulink model of six-phase drive with indirect field-oriented control and simulated with two types PID and PID fuzzy controllers. The simulation results, reflecting step response and provided speed reference law as well as load application at constant speed, are presented and analyzed. Fuzzy controller with 49 rules is considered and applied. Six-phase electric drive with field-oriented control at well-tuned PID controller has good step response specifications: settling time 0.13 s at starting without load, step response has no overshoot, small about 1 % short time decrease of speed at load of 5 Nm application. The system employing PID fuzzy controller shows slightly better results in response to torque application: the decrease in speed 1 % is eliminated in shorter time. Simulation results indicate the robustness of both systems with six-phase drive to disturbance and provide high quality transient specifications at determined reference speed.

The article discusses issues related to the optimization of the operation of medium voltage networks, focusing on the optimization of network division points and the selection of the optimal network configuration aimed at reducing power and energy losses. The article provides a critical review of selected scientific works from recent years on the issue of network configuration optimization. The developed solution uses non-deterministic network models and statistical and probabilistic approaches. Optimization studies were carried out using heuristic optimization methods and proprietary algorithms for forecasting the load and power generated from renewable energy sources. The solution proposed in the article has many application possibilities. It can be used to optimize the location of network division points (reducing economic costs related to the modernization of network infrastructure) as well as to optimize the reconfiguration process in order to reduce power losses while maintaining voltage criteria.

Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate ("hallucinated") content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources. However, in the healthcare domain there is currently a lack of systematic understanding of which datasets, RAG methodologies and evaluation frameworks are available. This review aims to bridge this gap by assessing RAG-based approaches employed by LLMs in healthcare, focusing on the different steps of retrieval, augmentation and generation. Additionally, we identify the limitations, strengths and gaps in the existing literature. Our synthesis shows that proprietary models such as GPT-3.5/4 are the most used for RAG applications in healthcare. Also, there is a lack of standardized evaluation frameworks for RAG-based applications. In addition, the majority of the studies do not assess or address ethical considerations related to RAG in healthcare. It is important to account for ethical challenges that are inherent when AI systems are implemented in the clinical setting. Lastly, we highlight the need for further research and development to ensure responsible and effective adoption of RAG in the medical domain.

Molnupiravir, a prodrug known for its broad antiviral activity, demonstrated efficacy in animal models of Covid-19, prompting clinical trials where initial results indicated a significant effect against the disease. However, subsequent clinical studies did not confirm these findings, leading to the rejection of molnupiravir for permanent market authorization in many countries. This report critically assessed 19 studies published in 17 reports that investigated the efficacy of molnupiravir in animal models of Covid-19 with the purpose of determining how well the design of these models informed human studies. We found that the administered doses of molnupiravir in most studies involving animal Covid-19 models were disproportionately higher than the dose rec-ommended for human use. Specifically, when adjusted for body surface area, half of the doses of molnupiravir used in the animal studies were more than twice as high as the human dose. Addi-tionally, the drug was frequently given prophylactically or shortly after SARS-CoV-2 inoculation in these models, in contrast to clinical trials where such timing is not consistently achieved. Furthermore, the recommended five-day treatment duration for humans was exceeded in several animal studies. Collectively, we suggest that these design elements in the reported animal studies contributed to a bias favoring molnupiravir, and thus inflated expectations for its efficacy against Covid-19. Addressing these elements may offer avenues to enhance the clinical efficacy of mol-nupiravir for treatment of Covid-19 that include dose increment, early treatment, and admin-istration by inhalation along with use of molnupiravir in antiviral combination therapy.

In this study, we investigated the features of co-infection with SARS-CoV-2 and nonpathogenic strain LEV8 of coxsackievirus A7 or enterovirus A71 for Vero E6 cells and Syrian hamsters. The investigation of co-infection with SARS-СoV-2 and LEV-8 or EV-A71 in the cell model was shown that a competitive inhibitory effect for these viruses were especially significant against SARS-CoV-2. Pre-infection with enteroviruses in animals caused more than a 100-fold decrease in the levels of SARS-CoV-2 virus replication in the respiratory tracts and more rapid clearance of the lower respiratory tract from infectious SARS-CoV-2. Co-infection with SARS-CoV-2 and LEV-8 or EV-A71 also reduced the severity of clinical manifestations of the SARS-CoV-2 infection for animals. Additionally, the histological data illustrated that co-infection with nonpathogenic coxsackievirus decreased the level of pathological changes by SARS-CoV-2 in the lungs. Research into the chemokine/cytokine profile demonstrated that the studied enteroviruses efficiently triggered this part of antiviral immune response, which can be associated with significant inhibition of SARS-CoV-2 infection. These results demonstrate that there exists a strong viral interference between the studied nonpathogenic strain of coxsackievirus A7 or enteroviruses A71 and SARS-CoV-2 pathogenic for humans in vitro and in vivo.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly within social media platforms like X (formerly Twitter) and in addressing cyberbullying present on them. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of Transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Leveraging state-of-the-art LLMs such as ChatGPT-4o, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and quantized for multilabel classification (detecting both biases and cyberbullying). The study's outcomes include a prototype of a hybrid application that combines a Bias Data Detector and a Bias Data Generator, validated through extensive testing.

Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of iot data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan 'an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase of sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control.

Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can integrate seamlessly into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms, using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual-semantic gap, steering the model towards enhanced performance in zero-shot instance retrieval. This work not only benchmarks the cognition alignment of AI, but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.}

Abstract This study delved into the application of various machine learning (ML) models for the classification of lung cancer levels. Through meticulous monitoring of parameters such as minimum child weight and learning rate, efforts were made to mitigate overfitting while optimizing model performance. The Deep Neural Network (DNN) emerged as a standout performer, showcasing robust performance across training, validation, and testing stages. Ensemble methods like voting and bagging also demonstrated promising results. However, Support Vector Machine (SVM) models with the Sigmoid kernel faced challenges in achieving satisfactory performance. Overall, the investigation sheds light on the efficacy of different ML models in lung cancer level classification and underscores the importance of parameter tuning to address overfitting concerns.

Ice formation on aircraft surfaces poses significant safety risks, and current detection systems often struggle to provide accurate, real-time predictions. This paper presents the development and comprehensive evaluation of a smart ice control system using a suite of machine learning models. The system utilizes various sensors to detect temperature anomalies and signal potential ice formation. We trained and tested supervised learning models (Logistic Regression, Support Vector Machine, Random Forest), unsupervised learning models (K-means clustering), and neural networks (Multi-Layer Perceptron) to predict and identify ice formation patterns. Experimental results demonstrate that our smart system, driven by machine learning, accurately predicts ice formation in real-time, optimizes de-icing processes, and enhances safety while reducing power consumption. This solution holds the potential for improving ice detection accuracy in aviation and other critical industries requiring robust predictive maintenance.

We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as the group orthogonal greedy algorithm and the high-dimensional Akaike information criterion to mitigate the impact of irrelevant predictors. Moreover, a novel technique, called peeling, is used to boost the variable selection procedure so that many factor-relevant predictors can be included in prediction. Finally, the supervised dynamic principal component analysis (sdPCA) method is adopted to account for the dynamic information in factor recovery. In simulation studies, we found that the proposed method adapts well to unknown degrees of sparsity and factor strength, which results in good performance even when the number of relevant predictors is large compared to the sample size. Applying to economic and environmental studies, the proposed method consistently performs well compared to some commonly used benchmarks in one-step-ahead out-sample forecasts.

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

This study investigates the effects of xylitol, a natural sugar alcohol, on tumor progression in syngeneic mouse cancer models. Xylitol has been known for its dental health benefits, but emerging evidence suggests broader biological roles, including potential anti-cancer properties. We explored xylitol's impact on two mouse cancer models: 4T1 mammary carcinoma and B16F10 melanoma. Xylitol's efficacy in inhibiting cancer cell lines and modulating tumor progression was assessed using immunocompetent female mice. The experiments involved intratumoral and peri-tumoral administration of a 20% xylitol solution in two mouse strains: BALB/c (4T1 mammary carcinoma) and C57BL/6 (B16F10 melanoma). Tumor volume, histopathology, and metabolomic analyses were conducted to gauge xylitol's influence. The study revealed that xylitol administration initially reduced tumor growth in the B16F10 melanoma model, accompanied by alterations in tumor metabolism. However, similar effects were not observed in the 4T1 mammary carcinoma model, and melanoma tumor growth re-commenced after stroma deterioration permitted xylitol solution leakage. These findings suggest that xylitol may have potential as an adjunct therapy in cancer treatment, specifically in melanoma. The differential response between the two cancer models underscores the complexity of cancer biology and the need for further investigation into xylitol's mechanisms of action and its role in cancer therapy.

The speed-density or flow-density relationship is a fundamental aspect of traffic flow theory. Single-regime models often face challenges in consistently fitting empirical data under diverse traffic conditions. This paper addresses the inaccuracies of single-regime models, attributing them not only to their functional forms but also to data dispersion and sample selection bias. To mitigate these issues, the data is segmented into day and night periods, leading to the creation of separate models for each. Calibration results, conducted on data from two freeways, Tehran-Karaj and Tehran-Qom, reveal distinct challenges, with the former exhibiting high dispersion and the latter showing low dispersion. Surprisingly, single-regime models demonstrate notable effectiveness when applied to distinct day and night data. Further analysis, categorizing data into day and night, results in reduced errors (20-60% improvement in R-Square), emphasizing the potential for enhanced accuracy by considering distinct regimes. Overall, the study underscores the significance of regime-specific considerations for accurate traffic flow modeling. Upon thorough evaluation, the study highlights the superiority of the neural network Multi-layer Perceptron (MLP) over traditional models. The research discusses the implications of incorporating day and night parameters into the models, emphasizing their potential for accuracy enhancement.

This study proposes a short- and medium-term electricity consumption prediction algorithm by combining the GRU model suitable for long-term forecasting and the Prophet model suitable for seasonality and event handling. (1) Manufacturing Company B's Electricity consumption data and meteorological data in Naju, Jeollanam-do, South Korea are collected and preprocessed. (2) The preprocessed data proposes the Prophet model in the first step for seasonality and event handling prediction. (3) In the second step, seven multivariate data are experimented with GRU. Specifically, the seven multivariate data consist of six meteorological data and the residuals between the predicted data from the proposed Prophet model in Step 1 and the observed data. These are utilized to predict electricity consumption at 15-minute intervals. (4) Electricity consumption is predicted for short-term (2 days and 7 days) and medium-term (15 days and 30 days) scenarios. The experimental results demonstrate that the proposed method outperforms the conventional Prophet model by more than 23 times and the modified GRU model by more than 2 times in terms of MAPE.

EIMIs face the challenge of adapting to the Paris Agreement and the European Green Deal to achieve carbon neutrality. This change is being driven by consumer interest in low-carbon products, regulatory requirements from climate agreements and investors prioritizing sustainability. This pressure is pushing companies to go beyond short-term financial gains and integrate sustainability into their business. The growing awareness of the environmental impact of business models (BM) has fueled the development of Sustainable Business Models (SBMs). This study aims to link the discussion on the role of SBMs in the drivers of decarbonization, to improve the understanding of how the drivers can be integrated and enable new BMs for EIMIs. Based on a structured literature review, semi-structured interviews were conducted in the EIMIs. The study identifies BM-components that are influenced by these drivers and require sustainable business model innovation (SBMI). The results show that decarbonization drivers have a significant impact on all BM components. Experts emphasize the importance of collaborative approaches and cooperation throughout the value chain. This research elaborates on the importance of systematic analysis to understand how companies can effectively manage decarbonization drivers and suggests in-depth exploration within BM-research to uncover potential SBM design options.

In this short communication, a concept has been presented to model geographical data to predict future temperature of Tabuk, region. Machine learning has been applied to the weather station data to develop a prediction model. The preliminary results are promising and encouraging and are envisaging to further this research towards the determination of unknown temperature rise in the region. This is important to mention here, that the problem has been formulated as a Regression problem, NOT as a classification problem. Hence, applying Convolutional neural networks is not possible, due to the non-existence of classes or converting the temperature values to classes does not make any sense. Hence, this is defined as a regression problem which achieved encouraging desirable results.

The proton exchange membrane fuel cell (PEMFC) is a critical and essential component of zero-emission electro-hydrogen generators. An accurate prediction of its performance is important for optimal operation management and preventive maintenance of these generators. However, the prediction is not simple because the PEMFCs have complex electrochemical reactions with multiple nonlinear relations between operating variables as inputs and voltage as output. In this paper, we propose an efficient prediction approach based on XGBRegressor and Tree-structured Parzen Estimator. In addition, to better select relevant features, Kernel Principal Component Analysis and Mutual Information are jointly used. The proposed approach allows consideration of the dynamic operating conditions of the PEMFC. To test and validate the robustness of the proposed approach, a real data-set of ten PEMFCs was considered. Furthermore, a comparison study with traditional machine learning models, such as artificial neural networks and support vector machine regression, was investigated. The obtained results confirm that our model outperforms the considered traditionel machine learning models.

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

Appropriate animal models of human diseases are a cornerstone in the advancement of science and medicine. To create animal models of neuropsychiatric and neurobehavioral diseases such as ASD necessitates the development of sufficient neurobehavioral measuring tools to translate human behavior to expected measurable behavioral features in animals. Indeed, at least in rodents, adequate neurobehavioral and neurological tests have been developed. Since ASD is characterized by a number of specific behavioral trends with significant severity, animal models of autistic-like behavior have to demonstrate the specific characteristic features, namely impaired social interactions, and communication, restricted, repetitive behavioral patterns with association to several additional impairments such as somatosensory, motor and memory impairments. Thus, an appropriate model must show behavioral impairment of a minimal number of neurobehavioral characteristics using an adequate number of behavioral tests. The proper animal models enable the study of ASD like-behavior from the etiologic, pathogenetic and therapeutic aspects. From the etiologic aspects, models have been developed by the use of immunogenic substances like polyinosinic-polycytidylic acid (PolyIC), Lipopolysaccharide (LPS) and propionic acid or other well-documented immunogens or pathogens, like mycobacterium tuberculosis. Another approach is the use of chemicals like valproic acid, Polychlorinated biphenyls (PCBs), organophosphate pesticides - chlorpyrifos (CPF) and others. These substances were administered either prenatally, generally after the period of major organogenesis, or, especially in rodents, during the early postnatal life. In addition, using modern genetic manipulation methods, genetic models have been created of almost all human genetic diseases that are manifested by autistic like behavior (i.e. fragile X, Rett syndrome, SHANK gene mutation Neuroligin genes and others). Ideally, we should not only evaluate the different behavioral modes affected by the ASD-like behavior but also assess the severity of the behavioral deviations by an appropriate scoring system, as applied to humans.

This study presents a groundbreaking approach in the field of gaming AI, focusing on the classic game” Pacman” through the lens of DRL integrated with advanced optimization techniques. The core of the research involves adapting DRL models for” Pacman”, utilizing the ESO for hyper- parameter tuning. These novel adaptations significantly enhance the AI agent’s performance, demonstrating a remarkable improvement in adaptability, responsiveness, and efficiency within the game environment. A pivotal aspect of this research is the innovative integration of metaheuristic optimization techniques into the DRL framework, a first in the domain of Atari gaming AI. This integration has proven essential in fine-tuning the DRL models, leading to a more effective and dynamic gaming experience. The study thoroughly evaluates and compares the performance of these algorithms, providing empirical support for their effectiveness and setting a new benchmark in AI-driven game development. The implications of this research extend beyond gaming AI, opening up possibilities for future exploration in several directions. Expanding DRL models to other complex gaming environments, continuous algorithmic enhancements, real-time learning adaptations, and applying these principles to robotics and autonomous vehicles are examples. The research also emphasizes ethical and responsible AI use in gaming to address fairness and addiction issues.

A review of quantitative risk assessment (QRA) models of L. monocytogenes in produce was carried out, with the objective of appraising and contrasting the effectiveness of the control strategies placed along the food chains. Despite nine of the thirteen QRA models recovered focused on fresh or RTE leafy greens, none of them represented important factors or sources of contamination in the primary production, such as type of cultivation, water, fertilisers or irrigation method/practices. Cross-contamination at processing and during consumer’s handling was modelled using transfer rates, which were shown to moderately drive the final risk of listeriosis; highlighting therefore the importance of accurately representing the transfer coefficient parameters. Many QRA models coincided in the fact that temperature fluctuations at retail or temperature abuse at home were key factors contributing to increasing the risk of listeriosis. In addition to a primary module that could help assess current on-farm practices and potential control measures, future QRA models for minimally-processed produce should also contain a refined sanitisation module able to estimate the effectiveness of various sanitisers as a function of type, concentration and exposure time. Finally, L. monocytogenes growth in the products down the supply chain should be estimated by using realistic time-temperature trajectories, and validated microbial kinetic parameters, both of them currently available in the literature.

Due to the increasing use of computer equipment, institutions and companies face challenges, such as sensitive data leaks and the spread of hate speech, which have severe consequences for organizations and their employees. Addressing these challenges is essential to avoid financial losses, reputational damage, and impacts on the psychological health of those involved. This paper presents a solution based on microservices for monitoring computers used by employees in organizations, including capturing information from the equipment using spyware techniques and a web application for managing alerts. The solution seeks to detect data leaks, suspicious behaviour, and hate speech. The results of the evaluation indicate that the proposed solution has an efficient data capture time and can identify unwanted behaviour in a short period. The solution also includes applying prediction models to detect hate speech, achieving an average accuracy of approximately 87%. The performance, scalability, and security evaluation demonstrate that the solution is suitable for dealing with data leakage and hate speech challenges in the corporate environment.

Previously, we described a 19-base pair double-stranded RNA with 3'-trinucleotide overhangs, acting as immunostimulatory RNA (isRNA). This molecule demonstrated notable antiproliferative effects on cancer cells, inhibited tumor growth, and elicited immunostimulatory and antiviral responses by inducing cytokine and interferon production. Within this study, we compared the efficiency of lung fibrosis development, initiated in mice by BLM or LPS using different schemes of induction. Then we compared the effect of isRNA used in a preventive or therapeutic regimen on the development of fibrosis in selected BLM- and LPS-induced mouse models and showed that isRNA can be used in pathological conditions accompanied by the development of inflammation and the risk of fibrosis formation, without adverse side effects. Prophylactic regimen of isRNA application is beneficial for prevention of the development of pulmonary fibrosis.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

Building electric energy is characterized by a significant increase of its uses (e.g. vehicle charging), a rapidly declining cost of all related data collection and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting related issues. First, on the forecasting explainability, i.e. the ability to understand and explain to the user what shapes the forecast. To this extent we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that a fast training would allow for faster deployment of forecasting in real life solutions. And third, we explore the concept of counterfactual analysis on actionable features, i.e. features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. As for GP models, we have found comparable and in some cases superior accuracy when compared to its neural-network and time-series counterparts but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found, and report here on an important potential especially for practical, decision support, solutions of counterfactuals built on actionable features and short training timeframes.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of the electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and can not outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with rolling 3-year window and compared the results with the RNNs. In our experiments, 3-layered GRUs outperformed all other neural network structures and state of the art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

In this study, we have first studied the trend in meteorological data from the Harmaleh, Vanai and Farsesh stations in the 50-year period in the Dez catchment area. The meteorological data will be then forecasted using SWAT and Mann-Kendall. Forecasting the results in the Mann-Kendall and SWAT model has been done using the code written in MATLAB software and RCP (4.5, 8.5) scenarios, respectively. Studying the results of the trend in the data of meteorological stations in this catchment area indicated that these two parametric and non-parametric methods have been used to determine trends in meteorological data. The results of the parametric method are positive in all meteorological parameters. Non-parametric method over a period of 50 years shows the presence of trends in the data. The comparison on the forecasting results at maximum temperature suggested that during summer, we will see an increase in temperature compared to the ground state in all three forecasts. The results of the minimum temperature forecast show a decrease in the minimum increase during the winter and the precipitation forecast indicates that at the end of autumn (Nov) precipitation decreased by 20 mm in the Mann-Kendall and 4.5 RCP while RCP8.5 suggests the increase in precipitation compared to the ground state. Studying the runoff forecast results using SWOT show that at the end of winter (Feb) and almost all spring (Mar, Apr) a decrease of about 40%, 15% and 14% will be seen, respectively

This paper analyzes and compares models for predicting average magnet losses in interior permanent-magnet motors with fractional-slot concentrated windings due to harmonics in the armature reaction (assuming sinusoidal phase currents). Particularly, loss models adopting different formulations and solutions to the Helmholtz equation to solve for the eddy currents are compared to a simpler model relying on an assumed eddy-current distribution. Boundaries in terms of magnet dimensions and angular frequency are identified (numerically and using an identified approximate analytical expression) to aid the machine designer whether the more simple loss model is applicable or not. The assumption of a uniform flux-density variation (used in the loss models) is also investigated for the case of V-shaped and straight interior permanent magnets. Finally, predicted volumetric loss densities are exemplified for combinations of slot and pole numbers common in automotive applications.

This work focuses on short-term forecasting of PV output power. The multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with good success. Their performances in forecasting PV power have been compared with other algorithms but not with themselves. The study aims to compare performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions. The performance of MLPNN, CNN and kNN are being compared using solar PV (hourly) energy yield data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in south Africa that may be prone to extreme weather conditions. The kNN algorithm was found to have RMSE value of 4.95% and MAE value of 2.74% at its worst performance, and RMSE value of 1.49% and MAE value of 0.85% at its best performance. It outperformed the others by a good margin and kNN could serve as a fast, easy, and accurate tool to forecast solar PV output power.