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Open AccessEditor’s ChoiceArticle
Energies 2017, 10(10), 1525;

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Author to whom correspondence should be addressed.
Received: 23 August 2017 / Revised: 25 September 2017 / Accepted: 25 September 2017 / Published: 7 October 2017
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
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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. View Full-Text
Keywords: building energy consumption; deep learning; stacked autoencoders; extreme learning machine building energy consumption; deep learning; stacked autoencoders; extreme learning machine

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Li, C.; Ding, Z.; Zhao, D.; Yi, J.; Zhang, G. Building Energy Consumption Prediction: An Extreme Deep Learning Approach. Energies 2017, 10, 1525.

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