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A bottom-up and procedural calibration method for building energy simulation models based on hourly electricity submetering data

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  • Ji, Ying
  • Xu, Peng

Abstract

BESMs (building energy simulation models) play an important role in the design, optimization and retrofit of buildings. Developing a BESM is relatively simple in the building design phase because nearly all inputs are known from design parameters. However, in the building operation phase, developing and calibrating a BESM becomes difficult because all operating parameters must be adjusted according to real-time data. All of these parameters are difficult to measure, and they vary over time. Existing calibration methods of BESMs, which involve hundreds of input parameters, lack standard procedures and require specialized engineers. Engineers must randomly adjust input parameters until the output energy use matches measured energy use. To solve the problem above, a new calibration approach with a detailed procedure is proposed in this paper. This approach relies on electricity submetering data and HVAC (Heating, Ventilation and Air Conditioning) cooling/heating loads. These data are becoming more available in commercial buildings. A case study is demonstrated in a large commercial building with satisfying results. The CV (coefficient of variation) and MBE (mean bias error) of the total hourly electricity consumption simulation, excluding HVAC, are 4% and 3%, respectively. The CVs of an HVAC system are 12% (chiller), 6% (pump) and 5% (fan), and the MBEs are 10% (chiller), 5% (pump) and 4% (fan).

Suggested Citation

  • Ji, Ying & Xu, Peng, 2015. "A bottom-up and procedural calibration method for building energy simulation models based on hourly electricity submetering data," Energy, Elsevier, vol. 93(P2), pages 2337-2350.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:2337-2350
    DOI: 10.1016/j.energy.2015.10.109
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    References listed on IDEAS

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    1. Akbari, H., 1995. "Validation of an algorithm to disaggregate whole-building hourly electrical load into end uses," Energy, Elsevier, vol. 20(12), pages 1291-1301.
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    Cited by:

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    2. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
    3. Nam-Kyu Kim & Myung-Hyun Shim & Dongjun Won, 2018. "Building Energy Management Strategy Using an HVAC System and Energy Storage System," Energies, MDPI, vol. 11(10), pages 1-15, October.
    4. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
    5. Chen, Yixing & Deng, Zhang & Hong, Tianzhen, 2020. "Automatic and rapid calibration of urban building energy models by learning from energy performance database," Applied Energy, Elsevier, vol. 277(C).
    6. Henrik Lund & Finn Arler & Poul Alberg Østergaard & Frede Hvelplund & David Connolly & Brian Vad Mathiesen & Peter Karnøe, 2017. "Simulation versus Optimisation: Theoretical Positions in Energy System Modelling," Energies, MDPI, vol. 10(7), pages 1-17, June.
    7. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
    8. 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.
    9. Cui, X. & Islam, M.R. & Chua, K.J., 2019. "Experimental study and energy saving potential analysis of a hybrid air treatment cooling system in tropical climates," Energy, Elsevier, vol. 172(C), pages 1016-1026.

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