Green Energy AI Cloud: Intelligent Community-Based Electricity Management System

According to the U.S. Energy Information Administration, 60 percent of the world’s electricity is generated from fossil fuels, 18 percent from nuclear power, and only 21 percent from green energy resources. These statistics highlight the pressing need to transition to renewable energy for a sustainable future. However, a major challenge that arises from this increased demand for green energy is the risk of electricity shortage. Overcoming this challenge requires a shift from conventional energy management to an AI-based electricity management system that can more efficiently balance supply and demand. In this paper, we have developed a comprehensive cloud-based Green AI service Management System to accommodate three important aspects of green energy services: electricity consumption, electricity generation, and shortage analysis. In this system, we propose using a community-based model that manages and analyzes multiple buildings’ energy usage, allowing the model to be trained further via distributed and aggregated training. The models developed for these components use three approaches: deep learning, machine learning, and reinforcement algorithms, and achieved a shortage analysis accuracy of 98.2 percent. The model is deployed on the cloud and results are accessible from the user interface. Overall, we provide a unique Green AI solution to the market using a system that can handle various aspects of green energy and gives better accuracy than existing systems.