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A novel resistance-capacitance model for evaluating urban building energy loads considering construction boundary heterogeneity

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  • Wang, Xiaoyu
  • Tian, Shuai
  • Ren, Jiawen
  • Jin, Xing
  • Zhou, Xin
  • Shi, Xing

Abstract

To address the problems associated with the accuracy of existing resistance-capacitance (RC)-based urban building energy modeling (UBEM) being low, a novel RC-based UBEM was developed. This model takes into account construction boundary heterogeneity by clustering and lumping together building construction elements with similar boundary conditions. In addition to buildings with single thermal zone, the novel RC model was extended to buildings with multiple thermal zones by using voltage-controlled voltage sources (VCVSs). The model was validated by four ASHRAE 140 cases with single thermal zone and two real urban building cases with multiple thermal zones. To verify the effectiveness of the proposed lumping concept, comparisons between the proposed RC model and the existing RC models were conducted. The results show that the novel RC-based UBEM could reach the balance between accuracy and efficiency. The hourly heating/cooling loads of the novel RC model agree well with those produced by the benchmark model EnergyPlus. The novel model has shown an efficiency improvement of approximately 78% compared to EnergyPlus. The 4R1C model is more accurate than 3R2C model especially for cases with large capacitance and resistance differences between interior and exterior construction components. The accuracy of the proposed RC-based UBEM is significantly improved compared with the existing RC-based UBEM. Thus, it is necessary to consider the construction boundary heterogeneity in the RC models for evaluating urban building energy loads.

Suggested Citation

  • Wang, Xiaoyu & Tian, Shuai & Ren, Jiawen & Jin, Xing & Zhou, Xin & Shi, Xing, 2024. "A novel resistance-capacitance model for evaluating urban building energy loads considering construction boundary heterogeneity," Applied Energy, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002794
    DOI: 10.1016/j.apenergy.2024.122896
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    References listed on IDEAS

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    1. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2020. "Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls," Energies, MDPI, vol. 13(12), pages 1-18, June.
    2. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
    3. Prataviera, Enrico & Vivian, Jacopo & Lombardo, Giulia & Zarrella, Angelo, 2022. "Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 311(C).
    4. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    5. Hu, Yuqing & Cheng, Xiaoyuan & Wang, Suhang & Chen, Jianli & Zhao, Tianxiang & Dai, Enyan, 2022. "Times series forecasting for urban building energy consumption based on graph convolutional network," Applied Energy, Elsevier, vol. 307(C).
    6. 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.
    7. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    9. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    10. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
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    1. Yucheng Guo & Jie Shi & Tong Guo & Fei Guo & Feng Lu & Lingqi Su, 2024. "Grey-Box Method for Urban Building Energy Modelling: Advancements and Potentials," Energies, MDPI, vol. 17(21), pages 1-25, October.

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