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Fast and accurate district heating and cooling energy demand and load calculations using reduced-order modelling

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  • Kim, Eui-Jong
  • He, Xi
  • Roux, Jean-Jacques
  • Johannes, Kévyn
  • Kuznik, Frédéric

Abstract

Recent developments in building energy models for urban energy simulation are primarily based on bottom-up modelling (N models used for N buildings). This work aims to develop a single assembled model for multiple buildings for convenient use in detailed urban analysis. The proposed model exhibits state-space model formalism, and a state-size reduction technique is applied to maintain model accuracy, even for a low-order representation. To accelerate the calculation time and ensure numerical stability, a direct solver is proposed to eliminate the iterative calculations required in Dymola for annual load calculations. The results of the proposed reduced model are in good agreement with the reference model. For a test case of ten buildings, a 2nd order reduced model (i.e., 2 differential equations) with the proposed direct solver can predict accurately the dynamic energy behaviour, resulting in an error of about 0.43% for the annual loads.

Suggested Citation

  • Kim, Eui-Jong & He, Xi & Roux, Jean-Jacques & Johannes, Kévyn & Kuznik, Frédéric, 2019. "Fast and accurate district heating and cooling energy demand and load calculations using reduced-order modelling," Applied Energy, Elsevier, vol. 238(C), pages 963-971.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:963-971
    DOI: 10.1016/j.apenergy.2019.01.183
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    References listed on IDEAS

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    1. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
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    Citations

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    Cited by:

    1. Liu, Guoqiang & Zhou, Xuan & Yan, Junwei & Yan, Gang, 2021. "A temperature and time-sharing dynamic control approach for space heating of buildings in district heating system," Energy, Elsevier, vol. 221(C).
    2. Zakula, Tea & Bagaric, Marina & Ferdelji, Nenad & Milovanovic, Bojan & Mudrinic, Sasa & Ritosa, Katia, 2019. "Comparison of dynamic simulations and the ISO 52016 standard for the assessment of building energy performance," Applied Energy, Elsevier, vol. 254(C).
    3. Lyons, Ben & O’Dwyer, Edward & Shah, Nilay, 2020. "Model reduction for Model Predictive Control of district and communal heating systems within cooperative energy systems," Energy, Elsevier, vol. 197(C).
    4. Pachauri, Nikhil & Ahn, Chang Wook, 2023. "Weighted aggregated ensemble model for energy demand management of buildings," Energy, Elsevier, vol. 263(PC).
    5. 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).
    6. Wei, Ziqing & Ren, Fukang & Zhu, Yikang & Yue, Bao & Ding, Yunxiao & Zheng, Chunyuan & Li, Bin & Zhai, Xiaoqiang, 2022. "Data-driven two-step identification of building thermal characteristics: A case study of office building," Applied Energy, Elsevier, vol. 326(C).
    7. Hinkelman, Kathryn & Wang, Jing & Zuo, Wangda & Gautier, Antoine & Wetter, Michael & Fan, Chengliang & Long, Nicholas, 2022. "Modelica-based modeling and simulation of district cooling systems: A case study," Applied Energy, Elsevier, vol. 311(C).

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