CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
Building Energy Consumption Prediction: An Extreme Deep Learning Approach
Li, Chengdong1; Ding, Zixiang1; Zhao, Dongbin2; Yi, Jianqiang2; Zhang, Guiqing1
Source PublicationENERGIES
2017-10-01
Volume10Issue:10Pages:1-20
SubtypeArticle
AbstractBuilding 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.
KeywordBuilding Energy Consumption Deep Learning Stacked Autoencoders Extreme Learning Machine
WOS HeadingsScience & Technology ; Technology
DOI10.3390/en10101525
WOS KeywordMULTIPLE LINEAR-REGRESSION ; ARTIFICIAL NEURAL-NETWORKS ; AUTOCORRELATION FUNCTION ; ELECTRICITY CONSUMPTION ; MACHINE ; MODELS
Indexed BySCI ; SSCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61473176 ; Natural Science Foundation of Shandong Province for Young Talents in Provincial Universities(ZR2015JL021) ; 61105077 ; 61573225)
WOS Research AreaEnergy & Fuels
WOS SubjectEnergy & Fuels
WOS IDWOS:000414578400080
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19311
Collection复杂系统管理与控制国家重点实验室_深度强化学习
Affiliation1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
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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