Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

Chengdong Li, Zixiang Ding, Jianqiang Yi, Yisheng Lv and Guiqing Zhang
Additional contact information
Chengdong Li: School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Zixiang Ding: School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Jianqiang Yi: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Yisheng Lv: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Guiqing Zhang: School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China

Energies, 2018, vol. 11, issue 1, 1-26

Abstract: To 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 methods—the 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.

Keywords: building energy consumption prediction; deep belief network; contrastive divergence algorithm; least squares learning; energy-consuming pattern (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/1/242/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/1/242/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:1:p:242-:d:127830

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().