Building Energy Consumption Prediction: An Extreme Deep Learning Approach

doi:10.3390/en10101525

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 深度强化学习

	Building Energy Consumption Prediction: An Extreme Deep Learning Approach
	Li, Chengdong 1; Ding, Zixiang1 ; Zhao, Dongbin2 ; Yi, Jianqiang2 ; Zhang, Guiqing 1
发表期刊	ENERGIES
	2017-10-01
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
DOI	10.3390/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：96[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	多模态人工智能系统全国重点实验室_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
推荐引用方式 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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