A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction

Numerous data-driven models have been successfully adopted for electrical energy consumption forecasting at building and larger scales. When the data set for forecasting is multi-sourced, heterogeneous or inadequate, single data-driven model may lead to convergence problem or poor model accuracy. The combination of advanced evolutionary algorithms (EAs) and data-driven models is proved effective in terms of prediction accuracy and robustness improvements. However, some of them are very time consuming to converge. In this paper, a novel EA, i.e. teaching learning based optimization (TLBO), is proposed for short-term building energy usage prediction. To enhance its convergence speed and optimization accuracy, the basic TLBO algorithm is further modified in three aspects. The improved algorithm is combined with artificial neural networks (ANNs) and applied to hourly electrical energy prediction of two educational buildings located in USA and China respectively. Performance comparisons show that the proposed model has superior performances than previously reported GA-ANN and PSO-ANN methods in terms of convergence speed and predictive accuracy, and is suitable for online energy prediction in the future.