Machine Learning for Building Energy Management

Future energy use prediction in buildings plays an important role in planning, managing, and saving energy. The complexity of building characteristics and occupants make the energy use prediction difficult. Because of its rapid learning characteristics, this study proposes machine learning (ML) models to predict the building energy consumption. The data set from non-residential buildings was collected to evaluate the predictive performance of the artificial neural network model (ANNs) and the support vector regression model (SVR). The evaluation results showed the effectiveness of the proposed machine learning model in predicting the energy usage during the next 24 hours of the building. The MAPE values obtained by the SVR model was 11.616%. The prediction results provide building managers with a use reference to saving energy consumption. This research contributes to highlight the advantages in the application of machine learning model in the field of construction