To develop a machine learning–driven system that accurately forecasts solar energy production and household electricity consumption, enabling smarter energy management through predictive analytics, web dashboards, and virtual assistant integration.

This project applies deep learning to forecast both solar energy production and household electricity consumption for smarter energy management. Using datasets from Malappuram (solar) and Delhi (consumption), models like ARIMAX, Prophet, and multivariate LSTM were compared, with LSTM delivering the best accuracy (Solar RMSE: 2.55 kWh, Consumption R²: 0.88). Data was enriched with NASA climate metrics and engineered features like seasonality and time-of-day patterns.
A proposed web-based dashboard will allow real-time visualization of forecasts, while an AI-powered virtual assistant will answer natural-language energy queries. This system aims to support households, utility providers, and sustainability initiatives by predicting trends, detecting anomalies, and optimizing energy usage.