A review on the prediction of building energy consumption

被引:1605
作者
Zhao, Hai-xiang [1 ]
Magoules, Frederic [1 ]
机构
[1] Ecole Cent Paris, Appl Math & Syst Lab, F-92295 Chatenay Malabry, France
关键词
Prediction; Building; Energy consumption; Engineering methods; Statistical models; Artificial intelligence; ARTIFICIAL NEURAL-NETWORKS; COOLING-LOAD PREDICTION; COMMERCIAL BUILDINGS; CONDITIONAL DEMAND; MODEL; PERFORMANCE; SIMULATION; SPACE; IDENTIFICATION; CONSERVATION;
D O I
10.1016/j.rser.2012.02.049
中图分类号
X [环境科学、安全科学];
学科分类号
083001 [环境科学];
摘要
The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference. (c) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:3586 / 3592
页数:7
相关论文
共 92 条
[1]
Aigner DJ, 1984, The Energy Journal, V5
[2]
Computer-aided building energy analysis techniques [J].
Al-Homoud, MS .
BUILDING AND ENVIRONMENT, 2001, 36 (04) :421-433
[3]
[Anonymous], 2010, Official Journal of European Union, VL153, P13
[4]
[Anonymous], 1997, P 17 INT C PAR ARCH
[5]
[Anonymous], 2010, P 2010 2 INT WORKSHO, DOI DOI 10.1109/IWISA.2010.5473608
[6]
[Anonymous], 2011, Building energy software tools directory: HAP
[7]
Ansari F. A., 2005, American Journal of Environmental Sciences, V1, P209, DOI 10.3844/ajessp.2005.209.212
[8]
Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks [J].
Aydinalp, M ;
Ugursal, VI ;
Fung, AS .
APPLIED ENERGY, 2004, 79 (02) :159-178
[9]
Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks [J].
Aydinalp, M ;
Ugursal, VI ;
Fung, AS .
APPLIED ENERGY, 2002, 71 (02) :87-110
[10]
Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector [J].
Aydinalp-Koksal, Merih ;
Ugursal, V. Ismet .
APPLIED ENERGY, 2008, 85 (04) :271-296