Renewable Electricity Management Cloud System for Smart Communities Using Advanced Machine Learning

Yukta Mehta, Vincent Lo, Vijen Mehta, Kunal Agrawal, Charan Teja Madabathula, Eugene Chang and Jerry Gao ()
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Yukta Mehta: Department of Applied Data Science, San Jose State University, 1 Washington Sq, San Jose, CA 95192, USA
Vincent Lo: Smart City Research Lab, San Jose State University, 1 Washington Sq, San Jose, CA 95192, USA
Vijen Mehta: Department of Applied Data Science, San Jose State University, 1 Washington Sq, San Jose, CA 95192, USA
Kunal Agrawal: Department of Applied Data Science, San Jose State University, 1 Washington Sq, San Jose, CA 95192, USA
Charan Teja Madabathula: Department of Applied Data Science, San Jose State University, 1 Washington Sq, San Jose, CA 95192, USA
Eugene Chang: ALPS Touchstone Inc., San Jose, CA 95134, USA
Jerry Gao: Department of Computer Engineering, San Jose State University, 1 Washington Sq, San Jose, CA 95192, USA

Energies, 2025, vol. 18, issue 6, 1-29

Abstract: 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.

Keywords: green AI; electricity shortage forecasting; consumption analysis; usage analysis; distributed model; aggregated model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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