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A review on the prediction of building energy consumption

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  • Zhao, Hai-xiang
  • Magoulès, Frédéric

Abstract

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.

Suggested Citation

  • Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    • Handle: RePEc:eee:rensus:v:16:y:2012:i:6:p:3586-3592
    DOI: 10.1016/j.rser.2012.02.049
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    References listed on IDEAS

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    1. Javeed Nizami, SSAK & Al-Garni, Ahmed Z, 1995. "Forecasting electric energy consumption using neural networks," Energy Policy, Elsevier, vol. 23(12), pages 1097-1104, December.
    2. Hou, Zhijian & Lian, Zhiwei & Yao, Ye & Yuan, Xinjian, 2006. "Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique," Applied Energy, Elsevier, vol. 83(9), pages 1033-1046, September.
    3. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
    4. Lee, Won-Yong & House, John M. & Kyong, Nam-Ho, 2004. "Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks," Applied Energy, Elsevier, vol. 77(2), pages 153-170, February.
    5. Zmeureanu, R., 2002. "Prediction of the cop of existing rooftop units using artificial neural networks and minimum number of sensors," Energy, Elsevier, vol. 27(9), pages 889-904.
    6. repec:aen:journl:1984v05-03-a06 is not listed on IDEAS
    7. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    8. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    9. Ben-Nakhi, Abdullatif E. & Mahmoud, Mohamed A., 2002. "Energy conservation in buildings through efficient A/C control using neural networks," Applied Energy, Elsevier, vol. 73(1), pages 5-23, September.
    10. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    11. Michael Parti & Cynthia Parti, 1980. "The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector," Bell Journal of Economics, The RAND Corporation, vol. 11(1), pages 309-321, Spring.
    12. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2004. "Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks," Applied Energy, Elsevier, vol. 79(2), pages 159-178, October.
    13. M.M. Gouda & S. Danaher & C.P. Underwood, 2002. "Application of an Artificial Neural Network for Modelling the Thermal Dynamics of a Building’s Space and its Heating System," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 8(3), pages 333-344, September.
    14. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
    15. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
    16. Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
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