A multi-task learning model for building electrical load prediction

Buildings are one of the largest energy-consuming sectors in the world. Accurate forecasting of building electricity loads can bring significant environmental and economic benefits by reducing electricity use and the corresponding greenhouse gas emissions. Deep learning has witnessed great success in the past decade, so this work proposes a deep learning model to predict the electrical loads of a commercial building. We perform a data exploration on the training set, and the results show that the electricity load is relevant to the temperature. Thus, this work proposes a deep learning model based on multi-task learning (MTL) architecture to predict the hourly electricity load. In our proposed architecture, the main task is to predict the electricity load, while the auxiliary task is to predict the outdoor temperature. The auxiliary task can provide additional regularization to the model to prevent overfitting. Furthermore, the proposed model comprises task-shared and task-specific layers, giving a base to learn cross-task and task-specific representations, respectively. We conduct experiments to assess our proposed model and compare it with other alternatives. The experimental results show that our proposed model can significantly outperform the comparison methods. Furthermore, the analysis shows that our proposed model can benefit from a simple ensemble technique to improve the prediction performance. To validate the generalization of the proposed model, we perform a robustness analysis on three additional datasets. We demonstrate that the proposed MTL approach can provide superior predictive accuracy and robustness.