A data-driven evidential regression model for building hourly energy consumption prediction with feature selection and parameters learning

Building energy consumption prediction is critical for building energy management and energy policy formulation, and its inherent uncertainty can significantly affect the utilization of current energy market benefits for market participants. To capture the uncertainty of energy consumption and enhance the predictive capability of the model, in this study, a data-driven evidential regression (EVREG) model with integrated feature selection function is proposed based on Dempster-Shafer theory and mutual information, which can perform point prediction and interval prediction for building hourly energy consumption to describe its fluctuation and uncertainty. Different from the traditional EVREG model, this method enables simultaneous feature selection and model parameters learning instead of treating feature selection as a separate data pre-processing step. Specifically, an evaluation function is defined to describe the significance of a candidate feature, taking into account the predictive power of regression model and the redundancy between the candidate feature and already selected features. According to a search strategy, features with high significance are selected to minimize the objective function. A real dataset from a commercial building is used to evaluate the performance of the proposed method. The results demonstrate that the proposed method can select fewer features while achieving better prediction performance compared to traditional feature selection methods used as data preprocessing. The proposed method also achieves better or comparable performance compared to commonly applied point prediction and interval prediction methods.