Efficient energy management has become increasingly important due to the growing demand for electricity, rising energy costs, and the need to reduce environmental impact. Accurate prediction of household energy consumption can help individuals and energy providers optimize energy usage, improve resource planning, and promote sustainable living. Traditional statistical forecasting methods often struggle to capture complex consumption patterns present in real-world energy datasets. With the advancement of artificial intelligence, machine learning techniques have shown strong potential for analysing energy consumption data and producing more accurate predictions. This study proposes a machine learning–based framework for predicting household energy consumption using historical electricity usage data. The system analyses various factors such as electrical current, voltage, frequency, and previous energy consumption values to forecast future energy usage. Multiple machine learning and deep learning models, including Linear Regression, Random Forest Regressor, LightGBM, XGBoost, CatBoost, LSTM, and BiLSTM, are implemented and evaluated to identify the most effective model for energy consumption prediction. In addition to prediction accuracy, the proposed framework integrates Explainable Artificial Intelligence (XAI) techniques to improve transparency and interpretability of model predictions. Explainability methods such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) are used to analyse the importance of different input features and understand how they influence the prediction results. Experimental results demonstrate that gradient boosting–based models provide highly accurate predictions, while XAI techniques help reveal the key factors that influence energy consumption patterns. The proposed system provides both accurate forecasting and interpretable insights, enabling users to better understand their energy usage behaviour. Such intelligent systems can support energy-efficient decision making, contribute to smart home energy management, and assist in the development of sustainable energy solutions.
A.Avinash et al. (Thu,) studied this question.