Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature

Abstract

Short-term forecasting of building energy consumption (BEC) is significant for building energy reduction and real-time demand response. In this study, we propose a new method to realize half-hourly BEC prediction. In this new method, to fully utilize the existing data features and to further promote the forecasting performance, we divide the BEC data into the stable (cyclic) and stochastic components, and propose a novel hybrid model to model the stable and stochastic components respectively. The cyclic feature (CF) is extracted via the spectrum analysis, while the stochastic component is approximated by a novel Deep Belief Network (DBN) and Extreme Learning Machine (ELM) based ensembled model (DEEM). This novel hybrid model is named DEEM + CF. Furthermore, two real-world BEC experiments are performed to verify the proposed method. Also, to display the superiorities of the proposed DEEM + CF, this model is compared with the DBN, DBN + CF, ELM, ELM + CF, Support Vector Regression (SVR) and SVR + CF. Experimental results indicate that the CF has a great influence on the promotion of forecasting accuracy for approximately 20%, and DEEM + CF performance is the best among the comparative models, with at least 3%, 6%, 10% better accuracy than the DBN + CF, ELM + CF and SVR + CF respectively under the criteria of MAE.