Energy Consumption Prediction & recommend system
Designed and developed a high-performance LightGBM predictive analytics core to forecast residential energy consumption by engineering robust temporal, calendar, and weather-based feature pipelines. Evaluated and optimized model performance through systematic regression metrics (RMSE, MAE, and R²) to ensure maximum forecasting accuracy. To bridge the gap between complex machine learning and user action, integrated SHAP-based Explainable AI (XAI) with a custom rule-based recommendation engine, effectively translating feature-level insights into personalized, actionable energy-saving suggestions. Additionally, built advanced financial and operational security modules, including an electricity bill estimator that maps consumption forecasts to real multi-slab tariff structures, and an automated anomaly detection system to instantly flag abnormal consumption patterns and potential hardware inefficiencies.
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