Interpretable Physics-Informed Neural Networks for energy consumption prediction using IoT sensors

Inefficient energy use in households contributes to higher domestic cost, underscores the need for effective management and predictive maintenance. Conventional forecasting techniques often lack real-time performance and accuracy that limit their practical utility. Our study introduces a novel IoT based framework that integrates machine learning (ML) and Physics Informed Neural Networks (PINNs) and explainable artificial intelligence (XAI) to enhance energy consumption prediction and interpretability. We evaluated our approach against 18 different models. The proposed PINNs model achieved an R-Square of 0.9972, with a Mean Absolute Error (MAE) of 0.07 and Root Mean Square (RMSE) of 0.03. These results outperform traditional machine learning and deep learning models, which typically range between R-Square of 0.89 to 0.94. The system leverages non-invasive IoT sensors for data collection which further processed via a Flask-based web sever and mobile app. Moreover, we used XAI tools (SHAP, LIME) to explain our model’s predictions by showing the contribution of each feature. The final deployment on web and mobile platforms ensures user accessibility, engagement and secure remote access through ngrok. Our research demonstrates PINNs’ effectiveness in delivering accurate, interpretable predictions and supporting sustainable smart energy management for residential settings.