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Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data

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  • Ji, Ying
  • Xu, Peng
  • Duan, Pengfei
  • Lu, Xing

Abstract

One major obstacle in Heating, Ventilation and Air Conditioning (HVAC) system Fault Detection and Diagnostics (FDD), retrofitting and energy performance evaluation is the lack of detailed hourly cooling load data. Cooling load measurement in commercial buildings is expensive and sometimes very difficult to implement. Detailed building simulation models, such as EnergyPlus, are too complicated to build and also must be calibrated. In this paper, an hourly cooling load prediction model, called the “RC-S” model, is proposed. This new cooling load calculation approach consists of a simplified thermal network model of the building envelope, a thermal network model for the building internal mass and the internal cooling load model from the submetering system. One existing RC model is introduced as reference model and three types of “RC-S” models are set up in this study. Genetic algorithm (GA) is selected to optimize the parameters in those models. Measurement data collected from a real commercial building and simulation data obtained from EnergyPlus model of the same commercial building are used to train and test the four models. The results prove that the proposed “RC-S” cooling load calculation method is more accurate than the existing RC model and much simpler than whole building simulation models. It can provide reasonable estimations of cooling loads for HVAC FDD and other performance evaluations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:169:y:2016:i:c:p:309-323
    DOI: 10.1016/j.apenergy.2016.02.036
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    7. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
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    9. Yan, Biao & Yang, Wansheng & He, Fuquan & Huang, Kehua & Zeng, Wenhao & Zhang, Wenlong & Ye, Haiseng, 2022. "Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact," Energy, Elsevier, vol. 255(C).
    10. Pop, Octavian G. & Fechete Tutunaru, Lucian & Bode, Florin & Abrudan, Ancuţa C. & Balan, Mugur C., 2018. "Energy efficiency of PCM integrated in fresh air cooling systems in different climatic conditions," Applied Energy, Elsevier, vol. 212(C), pages 976-996.
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    15. Abokersh, Mohamed Hany & Spiekman, Marleen & Vijlbrief, Olav & van Goch, T.A.J. & Vallès, Manel & Boer, Dieter, 2021. "A real-time diagnostic tool for evaluating the thermal performance of nearly zero energy buildings," Applied Energy, Elsevier, vol. 281(C).
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    18. Ping Wang & Guangcai Gong & Yan Zhou & Bin Qin, 2018. "A Simplified Calculation Method for Building Envelope Cooling Loads in Central South China," Energies, MDPI, vol. 11(7), pages 1-18, July.
    19. Giovanni Bianco & Stefano Bracco & Federico Delfino & Lorenzo Gambelli & Michela Robba & Mansueto Rossi, 2020. "A Building Energy Management System Based on an Equivalent Electric Circuit Model," Energies, MDPI, vol. 13(7), pages 1-23, April.
    20. Wang, Junke & Jiang, Yilin & Tang, Choon Yik & Song, Li, 2022. "Development and validation of a second-order thermal network model for residential buildings," Applied Energy, Elsevier, vol. 306(PB).
    21. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    22. Yue, Bao & Wei, Ziqing & Zheng, Chunyuan & Ding, Yunxiao & Li, Bin & Li, Dongdong & Liang, Xingang & Zhai, Xiaoqiang, 2023. "Power consumption prediction of variable refrigerant flow system through data-physics hybrid approach: An online prediction test in office building," Energy, Elsevier, vol. 278(PA).
    23. Ke, Bwo-Ren & Chung, Chen-Yuan & Chen, Yen-Chang, 2016. "Minimizing the costs of constructing an all plug-in electric bus transportation system: A case study in Penghu," Applied Energy, Elsevier, vol. 177(C), pages 649-660.

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