Building Energy Consumption Prediction: An Extreme Deep Learning Approach
Li, Chengdong1; Ding, Zixiang1; Zhao, Dongbin2; Yi, Jianqiang2; Zhang, Guiqing1
2017-10-01
发表期刊ENERGIES
卷号10期号:10页码:1-20
文章类型Article
摘要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.
关键词Building Energy Consumption Deep Learning Stacked Autoencoders Extreme Learning Machine
WOS标题词Science & Technology ; Technology
DOI10.3390/en10101525
关键词[WOS]MULTIPLE LINEAR-REGRESSION ; ARTIFICIAL NEURAL-NETWORKS ; AUTOCORRELATION FUNCTION ; ELECTRICITY CONSUMPTION ; MACHINE ; MODELS
收录类别SCI ; SSCI
语种英语
项目资助者National Natural Science Foundation of China(61473176 ; Natural Science Foundation of Shandong Province for Young Talents in Provincial Universities(ZR2015JL021) ; 61105077 ; 61573225)
WOS研究方向Energy & Fuels
WOS类目Energy & Fuels
WOS记录号WOS:000414578400080
引用统计
被引频次:19[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19311
专题复杂系统管理与控制国家重点实验室_深度强化学习
作者单位1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
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GB/T 7714
Li, Chengdong,Ding, Zixiang,Zhao, Dongbin,et al. Building Energy Consumption Prediction: An Extreme Deep Learning Approach[J]. ENERGIES,2017,10(10):1-20.
APA Li, Chengdong,Ding, Zixiang,Zhao, Dongbin,Yi, Jianqiang,&Zhang, Guiqing.(2017).Building Energy Consumption Prediction: An Extreme Deep Learning Approach.ENERGIES,10(10),1-20.
MLA Li, Chengdong,et al."Building Energy Consumption Prediction: An Extreme Deep Learning Approach".ENERGIES 10.10(2017):1-20.
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