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Methods for benchmarking building energy consumption against its past or intended performance: An overview

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  • Li, Zhengwei
  • Han, Yanmin
  • Xu, Peng

Abstract

Building sector consumes a significant portion of energy worldwide. One of the reasons is that the performance of building and its components degrades over the years. It is found that by improving the performance of existing systems through continuous commissioning, significant energy saving can be achieved. In a continuous commissioning process, energy benchmarking is extremely important for tracking, monitoring and detecting abnormal energy consumption behavior of a building. In this paper, up to date methods and tools available for energy benchmarking purpose are reviewed. It is hoped that with this paper, researchers and building operators are more confident in choosing a proper method (or tool) during the commissioning process.

Suggested Citation

  • Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
  • Handle: RePEc:eee:appene:v:124:y:2014:i:c:p:325-334
    DOI: 10.1016/j.apenergy.2014.03.020
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    References listed on IDEAS

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    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Xiao, Fu & Wang, Shengwei, 2009. "Progress and methodologies of lifecycle commissioning of HVAC systems to enhance building sustainability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 1144-1149, June.
    3. 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.
    4. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    5. Djuric, Natasa & Novakovic, Vojislav, 2009. "Review of possibilities and necessities for building lifetime commissioning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 486-492, February.
    6. Lin, Hung-Wen & Hong, Tianzhen, 2013. "On variations of space-heating energy use in office buildings," Applied Energy, Elsevier, vol. 111(C), pages 515-528.
    7. 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.
    8. Mui, K.W. & Wong, L.T. & Law, L.Y., 2007. "An energy benchmarking model for ventilation systems of air-conditioned offices in subtropical climates," Applied Energy, Elsevier, vol. 84(1), pages 89-98, January.
    9. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2014. "A novel dynamic modeling approach for predicting building energy performance," Applied Energy, Elsevier, vol. 114(C), pages 91-103.
    10. 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.
    11. Manfren, Massimiliano & Aste, Niccolò & Moshksar, Reza, 2013. "Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation," Applied Energy, Elsevier, vol. 103(C), pages 627-641.
    12. Wang, Liping & Greenberg, Steve & Fiegel, John & Rubalcava, Alma & Earni, Shankar & Pang, Xiufeng & Yin, Rongxin & Woodworth, Spencer & Hernandez-Maldonado, Jorge, 2013. "Monitoring-based HVAC commissioning of an existing office building for energy efficiency," Applied Energy, Elsevier, vol. 102(C), pages 1382-1390.
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