Renewable Electricity Management Cloud System for Smart Communities Using Advanced Machine Learning
Based on the renewable energy assessment in 2023, it was found that only 21% of total electricity is generated using renewable sources. As the global demand for electricity rises in the AI world, the need for electricity management will increase and must be optimized. Based on research, many compani...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/6/1418 |
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| Summary: | Based on the renewable energy assessment in 2023, it was found that only 21% of total electricity is generated using renewable sources. As the global demand for electricity rises in the AI world, the need for electricity management will increase and must be optimized. Based on research, many companies are working on green AI electricity management, but few companies are working on predicting shortages. To identify the rising electricity demand, predict the shortage, and to bring attention to consumption, this study focuses on the optimization of solar electricity generation, tracking its consumption, and forecasting the electricity shortages well in advance. This system demonstrates a novel approach using advanced machine learning, deep learning, and reinforcement learning to maximize solar energy utilization. This paper proposes and develops a community-based model that manages and analyzes multiple buildings’ energy usage, allowing the model to perform both distributed and aggregated decision-making, achieving an accuracy of 98.2% using stacking results of models with reinforcement learning. Concerning the real-world problem, this paper provides a sustainable solution by combining data-driven models with reinforcement learning, contributing to the current market need. |
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| ISSN: | 1996-1073 |