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Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction
Li, Chengdong1; Ding, Zixiang1; Yi, Jianqiang2; Lv, Yisheng2; Zhang, Guiqing1
Source PublicationENERGIES
AbstractTo enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN) based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD) algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity). In order to examine the advantages of the proposed model, four popular artificial intelligence methodsthe backward propagation neural network (BPNN), the generalized radial basis function neural network (GRBFNN), the extreme learning machine (ELM), and the support vector regressor (SVR) are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption prediction.
KeywordBuilding Energy Consumption Prediction Deep Belief Network Contrastive Divergence Algorithm Least Squares Learning Energy-consuming Pattern
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61473176 ; Natural Science Foundation of Shandong Province for Young Talents in Province Universities(ZR2015JL021) ; 61105077 ; 61573225)
WOS Research AreaEnergy & Fuels
WOS SubjectEnergy & Fuels
WOS IDWOS:000424397600241
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Document Type期刊论文
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,Yi, Jianqiang,et al. Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction[J]. ENERGIES,2018,11(1).
APA Li, Chengdong,Ding, Zixiang,Yi, Jianqiang,Lv, Yisheng,&Zhang, Guiqing.(2018).Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction.ENERGIES,11(1).
MLA Li, Chengdong,et al."Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction".ENERGIES 11.1(2018).
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