• Submit to Energies Login Register
• MDPI
• Journals A-Z
• For Authors
• For Editors
• About
• Open Access Policy

• Abstract
• Full-Text PDF [549 KB]

• Article Statistics
• Google Scholar
• Order Reprints

• Cite this article
• Export to BibTeX
• Export to EndNote

• [+] on DOAJ
• Yu, W
• Li, B
• Lei, Y
• Liu, M
• [+] on Google Scholar
• Yu, W
• Li, B
• Lei, Y
• Liu, M
• [+] on PubMed
• Yu, W
• Li, B
• Lei, Y
• Liu, M

•  CiteULike
•  Facebook
•  Mendeley
•  Twitter

This article is

• freely available
• re-usable

Energies 2011, 4(3), 475-487; doi:10.3390/en4030475

Article

Analysis of a Residential Building Energy Consumption Demand Model

Wei Yu ^1,2,* , Baizhan Li ^1,2 , Yarong Lei ^1,2  and Meng Liu ^1,2

¹ Key Lab of the Three Gorges Reservoir Region’s Eco-Environment, Chongqing University, Ministry of Education, Chongqing, 400045, China ² Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing, 400045, China

* Author to whom correspondence should be addressed.

Received: 31 January 2011; in revised form: 15 February 2011 / Accepted: 8 March 2011 / Published: 10 March 2011

(This article belongs to the Special Issue Energy Savings in the Domestic and Tertiary Sectors 2011)

Download PDF Full-Text [549 KB, uploaded 10 March 2011 09:32 CET]

Abstract: In order to estimate the energy consumption demand of residential buildings, this paper first discusses the status and shortcomings of current domestic energy consumption models. Then it proposes and develops a residential building energy consumption demand model based on a back propagation (BP) neural network model. After that, taking residential buildings in Chongqing (P.R. China) as an example, 16 energy consumption indicators are introduced as characteristics of the residential buildings in Chongqing. The index system of the BP neutral network prediction model is established and the multi-factorial BP neural network prediction model of Chongqing residential building energy consumption is developed using the Cshap language, based on the SQL server 2005 platform. The results obtained by applying the model in Chongqing are in good agreement with actual ones. In addition, the model provides corresponding approximate data by taking into account the potential energy structure adjustments and relevant energy policy regulations.

Keywords: energy consumption; energy consumption demand model; BP neural network; residential building

Article Statistics

Cite This Article