Next Article in Journal
Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms
Previous Article in Journal
Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid
Previous Article in Special Issue
A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Energies 2018, 11(1), 242; doi:10.3390/en11010242

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Author to whom correspondence should be addressed.
Received: 15 December 2017 / Revised: 16 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
View Full-Text   |   Download PDF [2475 KB, uploaded 19 January 2018]   |  


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. View Full-Text
Keywords: building energy consumption prediction; deep belief network; contrastive divergence algorithm; least squares learning; energy-consuming pattern building energy consumption prediction; deep belief network; contrastive divergence algorithm; least squares learning; energy-consuming pattern

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, C.; Ding, Z.; Yi, J.; Lv, Y.; Zhang, G. Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction. Energies 2018, 11, 242.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top