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This project demonstrates an AI system that optimizes energy flows in homes, suggesting ways to enhance sustainability.
The increasing integration of renewable energy sources such as solar and wind into residential systems has created the need for intelligent management solutions that can optimize energy generation and consumption. Traditional home energy systems lack predictive capabilities and fail to balance renewable production with varying household demand, resulting in inefficiency, grid dependency, and financial loss. Synapse Home proposes an AI-powered Smart Energy Management System that utilizes machine learning models to forecast solar and wind power generation along with household electricity consumption. These predictions enable automated energy flow decisions to maximize renewable utilization and minimize grid reliance. Users interact through a real-time dashboard that visualizes live power flow, forecast trends, and energy insights. Experiments using public renewable energy datasets demonstrate the model's accuracy and practical viability in short-term forecasting and energy optimization. The project showcases how artificial intelligence and data analytics can transform residential power management, reduce electricity costs, and promote sustainable energy use in modern smart homes.
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Nawde et al. (2026) studied this question.
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