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A multi-level energy performance diagnosis method for energy information poor buildings

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  • Yan, Chengchu
  • Wang, Shengwei
  • Xiao, Fu
  • Gao, Dian-ce

Abstract

A thorough assessment and diagnosis is critical for understating and enhancing building energy performance while most buildings cannot provide sufficient energy use data for a detailed diagnosis. This paper presents a multi-level energy performance diagnosis method for energy information-poor buildings where very limited energy use data are available. A simplified monthly energy performance calculation method based on basic energy balances within a building is developed. It provides sufficient energy performance data of a building at multiple levels (i.e., building, system and component levels) while only requiring monthly energy bill data and few in-situ measurements of the HVAC system. The energy performance level then can be determined by comparing the estimated performance data with the benchmark data. A customized benchmarking method using the “relative performance factor” is proposed to indicate the relative difference between the current performance and the expected performance, and to estimate the energy saving potentials. The developed multi-level energy performance calculation method is validated in a super high-rise building in Hong Kong. A case study on illustrating how to apply the proposed diagnosis method for identifying the poor performance areas and the causes behind as well as estimating the energy saving potentials is also presented.

Suggested Citation

  • Yan, Chengchu & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "A multi-level energy performance diagnosis method for energy information poor buildings," Energy, Elsevier, vol. 83(C), pages 189-203.
    • Handle: RePEc:eee:energy:v:83:y:2015:i:c:p:189-203
    DOI: 10.1016/j.energy.2015.02.014
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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. Diakaki, Christina & Grigoroudis, Evangelos & Kabelis, Nikos & Kolokotsa, Dionyssia & Kalaitzakis, Kostas & Stavrakakis, George, 2010. "A multi-objective decision model for the improvement of energy efficiency in buildings," Energy, Elsevier, vol. 35(12), pages 5483-5496.
    3. Ginestet, S. & Marchio, D., 2010. "Retro and on-going commissioning tool applied to an existing building: Operability and results of IPMVP," Energy, Elsevier, vol. 35(4), pages 1717-1723.
    4. 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.
    5. Ghiaus, Christian, 2013. "Causality issue in the heat balance method for calculating the design heating and cooling load," Energy, Elsevier, vol. 50(C), pages 292-301.
    6. Lee, Siew Eang & Rajagopalan, Priyadarsini, 2008. "Building energy efficiency labeling programme in Singapore," Energy Policy, Elsevier, vol. 36(10), pages 3982-3992, October.
    7. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    8. Pisello, Anna Laura & Goretti, Michele & Cotana, Franco, 2012. "A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity," Applied Energy, Elsevier, vol. 97(C), pages 419-429.
    9. Chung, William & Hui, Y.V. & Lam, Y. Miu, 2006. "Benchmarking the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 83(1), pages 1-14, January.
    10. Buonomano, Annamaria & Calise, Francesco & Ferruzzi, Gabriele & Palombo, Adolfo, 2014. "Dynamic energy performance analysis: Case study for energy efficiency retrofits of hospital buildings," Energy, Elsevier, vol. 78(C), pages 555-572.
    Full references (including those not matched with items on IDEAS)

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    9. Wang, Huilong & Xu, Peng & Lu, Xing & Yuan, Dengkuo, 2016. "Methodology of comprehensive building energy performance diagnosis for large commercial buildings at multiple levels," Applied Energy, Elsevier, vol. 169(C), pages 14-27.
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    11. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
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