A novel intelligent modeling and prediction of heat energy consumption in smart buildings

The rising cost and demand for energy have prompted several organizations to devise innovative methods for monitoring, managing, and conserving energy. Heat energy load prediction in smart buildings is critical for saving energy as well as for meeting energy demands. As the cost of energy and demand is increasing, people must find some way to control it and save energy. To overcome these issues, an intelligent energy management system needs to be developed to help individuals by predicting heat energy demand ahead of time and analyzing energy use patterns. This paper introduces a data-driven short-term heat energy consumption prediction for smart buildings by developing a hybrid deep learning model considering the capabilities of Convolutional and Recurrent Neural Networks. The proposed model extracts hidden patterns from actual data collected through sensors and also uses hand-crafted features to predict the heat energy demand accurately. The efficiency of the proposed model has been evaluated on real smart heat metering data, a case study in Denmark, having three kinds of buildings: single-family, apartment, and terraced houses. The extensive experimental results have been reported with real-world data by considering various evaluation metrics such as MAE, RMSE, MAPE and R-2. The results are promising and outperformed other state-of-the-art methods.