LAUSR

Abstract Short-term building energy usage prediction plays an important role in fields of building energy management, power plants dispatch, and peak demand conflicting with grid security. A large number of data-driven models were applied to building and larger scale energy consumption prediction in the past two decades. Although successes have been achieved by these models for specific cases, no single model has dominated others over all cases. To improve model’s prediction performance in applications to different cases, a scheme of ensemble learning is proposed in this study. This ensemble scheme includes a self-adaptive model package that fuses the characteristics of multiple individual models together. Total five swarm intelligence based data-driven models are chosen as primary predictors of the ensemble scheme. The recursive feature elimination (RFE) is used for essential feature selection, and the K-fold cross validation method is applied to avoid over-fitting problem. Two sets of real buildings’ electricity usage data are collected for the performance comparison. Case A is from energy prediction shooting contest I organized by the American Society of Heating Refrigerating and Air-conditioning Engineer (ASHRAE) and Case B is from a campus building at the University of Wyoming, USA. The results show that the accuracy of the proposed ensemble model is better than that of any individual base model in both cases. Due to the generalization ability, the proposed ensemble model has the potential to be the unified model for different building energy consumption prediction cases.