Predicting Energy Consumption in Smart Buildings Using Machine Learning and Environmental Variables

Over the past several decades, there has been a significant increase in energy consumption worldwide, with a substantial portion of this usage occurring in residential buildings. As a result, developing reliable tools for analyzing and forecasting energy consumption has become essential in the global effort to enhance sustainability. Machine learning (ML) has demonstrated exceptional performance in predictive tasks related to energy usage. In this paper, we evaluate and compare five ML and deep learning models to predict energy consumption in smart buildings, utilizing the publicly available KAG energy dataset. Our experimental results indicate that Long Short-Term Memory (LSTM) consistently outperforms Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Multi-layer Perceptron (MLP), and Extra Trees (ExtraTr) models, offering superior predictive accuracy and robustness.