A Cloud-Integrated Virtual Framework for LSTM-Driven Solar Forecasting and Residential Energy Management - Devanathan, B.; Suyampulingam, A.; Selvaraj, P.; Karuppasamy, Ilango; Ilamparithi, T. (IEEE access, 2025)
With the increasing dependence on renewable energy-particularly solar power-accurate forecasting and intelligent energy management, it has become essential for reducing grid dependency and optimizing energy usage. This paper presents an AI-powered renewable energy forecasting and scheduling system designed for smart homes. A Virtual platform is developed to predict next-day solar energy generation using real-time weather data obtained via Application Programming Interfaces (API) (such as., Open-Meteo), enabling hourly forecasts without the need for physical sensors. Various machine learning algorithms, including Lasso, Ridge, Support Vector Machine(SVM), Decision Tree, Random Forest, and Long Short-Term Memory (LSTM) networks, were trained and evaluated using historical data. The LSTM-based algorithm demonstrated the highest prediction accuracy and was selected as the core forecasting model. The forecasted solar energy values, at one-hour intervals, are dynamically used to schedule household appliances through a priority-based greedy algorithm. The algorithm prioritizes appliance loads based on criticality and energy availability, ensuring optimal solar utilization and minimizing reliance on grid power. The system is deployed via a user-friendly website with two interactive tabs, namely Forecast and Schedule, where users can select a date, view predicted solar generation, and receive an optimized appliance schedule. Experimental results indicate that intelligent scheduling using LSTM-based forecasting achieves up to 2.81% cost savings compared to conventional non-forecast-based methods. This scalable, sensor-free solution opens avenues for future enhancements, including multi-day forecasting and real-time optimization for broader smart energy applications.