Building Energy Consumption Prediction: An Extreme Deep Learning Approach

Chengdong Li, Zixiang Ding, Dongbin Zhao, Jianqiang Yi and Guiqing Zhang
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Chengdong Li: School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Zixiang Ding: School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Dongbin Zhao: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Jianqiang Yi: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Guiqing Zhang: School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China

Energies, 2017, vol. 10, issue 10, 1-20

Abstract: Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption.

Keywords: building energy consumption; deep learning; stacked autoencoders; extreme learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (50)

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