Modeling Short-Term Energy Consumption Forecasting with External Influences: A Comprehensive Study

Leonardo Amaral; Gustavo Araújo; Ricardo Moraes; Francisco Vasques; Paulo Portugal

doi:10.20944/preprints202406.1550.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 20 June 2024 / Approved: 21 June 2024 / Online: 21 June 2024 (16:54:00 CEST)

Short-term energy consumption forecasting crucial for the operation of new electrical en-ergy grids, not only due to their own characteristics, but also due to the new elements pre-sent in the energy matrix. Many existing models are limited by their methodologies or by considering only a narrow set of factors influencing energy consumption. This study in-troduces a pipeline for developing an energy forecasting model and evaluates the signifi-cance of factors affecting energy consumption and, consequently, forecast accuracy. The study utilizes a dataset from ISO NE (Independent System Operator New England), span-ning the total electric load of various cities in New England from January 2017 to December 2019. This dataset comprises 23 independent variables, including weather data, economic indicators, and market information. The results outline the steps involved in constructing energy forecast models using time series analysis. By carefully selecting variables and rep-resenting external factors, the study demonstrates the feasibility of generating more accu-rate predictions with reduced computational resources.

electricity forecasting model; deep learning; computational intelligence

Engineering, Energy and Fuel Technology

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Modeling Short-Term Energy Consumption Forecasting with External Influences: A Comprehensive Study

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