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Building Energy Consumption Prediction: An Extreme Deep Learning Approach
Dec 05, 2017Author:
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Title: Building Energy Consumption Prediction: An Extreme Deep Learning Approach

 Authors: Li, CD; Ding, ZX; Zhao, DB; Yi, JQ; Zhang, GQ

 Author Full Names: Li, Chengdong; Ding, Zixiang; Zhao, Dongbin; Yi, Jianqiang; Zhang, Guiqing

 Source: ENERGIES, 10 (10):10.3390/en10101525 OCT 2017

 Language: English

 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.

 ISSN: 1996-1073

 Article Number: 1525

 IDS Number: FL9MA

 Unique ID: WOS:000414578400080

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