Version 1
: Received: 17 June 2024 / Approved: 18 June 2024 / Online: 18 June 2024 (08:16:16 CEST)
How to cite:
Ali, S.; Hayat, K.; Hussain, I.; Khan, A.; Kim, D. Hybrid Energy Optimization for Efficient Distributed Energy Resources Management in Green Building Environment. Preprints2024, 2024061191. https://doi.org/10.20944/preprints202406.1191.v1
Ali, S.; Hayat, K.; Hussain, I.; Khan, A.; Kim, D. Hybrid Energy Optimization for Efficient Distributed Energy Resources Management in Green Building Environment. Preprints 2024, 2024061191. https://doi.org/10.20944/preprints202406.1191.v1
Ali, S.; Hayat, K.; Hussain, I.; Khan, A.; Kim, D. Hybrid Energy Optimization for Efficient Distributed Energy Resources Management in Green Building Environment. Preprints2024, 2024061191. https://doi.org/10.20944/preprints202406.1191.v1
APA Style
Ali, S., Hayat, K., Hussain, I., Khan, A., & Kim, D. (2024). Hybrid Energy Optimization for Efficient Distributed Energy Resources Management in Green Building Environment. Preprints. https://doi.org/10.20944/preprints202406.1191.v1
Chicago/Turabian Style
Ali, S., Ahmad Khan and Dohyeun Kim. 2024 "Hybrid Energy Optimization for Efficient Distributed Energy Resources Management in Green Building Environment" Preprints. https://doi.org/10.20944/preprints202406.1191.v1
Abstract
Without an established energy management plan, human lifestyle improvement is incomprehensible. Adequate energy assets are the key to human lifestyle development, however, energy assets are restricted and exorbitant too. In this paper, an energy control system for a green environment called PMC (Power Management and Control) is proposed. The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The blend of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is utilized to make a fusion methodology to improve occupant comfort index (OCI) and decrease energy utilization. The main theme of the proposed PMC technique is to improve OCI and decrease energy utilization. The proposed framework gives better OCI when compared with its counterpart Ant Bee Colony along with Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the proposed PMC methodology gives practically the same OCI, but consumes less energy. The proposed PMC methodology additionally accomplished the most extreme OCI (i-e 1) when compared with the existing model FA-GA (i-e 0.98). The proposed PMC model consumed less energy as compared to existing models ABCKB, GAP, PSO and AEO. The proposed model consumed more energy than SOHP but provided better OCI. The comparative outcomes show the viability of the proposed PMC framework in lessening energy utilization and improving the OCI. Unlike other existing mythologies except for the AEO framework, the proposed PMC technique is additionally confirmed through a simulated climate by controlling indoor climate using actuators, like Fan, light, AC and boiler.
Keywords
Energy consumption; energy efficiency; genetic algorithm and particle swarm optimization; occupants comfort index; prediction
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
