IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v238y2019icp963-971.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919301771
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.01.183?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Kohler, M. & Blond, N. & Clappier, A., 2016. "A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France)," Applied Energy, Elsevier, vol. 184(C), pages 40-54.
    4. Frayssinet, Loïc & Merlier, Lucie & Kuznik, Frédéric & Hubert, Jean-Luc & Milliez, Maya & Roux, Jean-Jacques, 2018. "Modeling the heating and cooling energy demand of urban buildings at city scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2318-2327.
    5. An, Jingjing & Yan, Da & Hong, Tianzhen & Sun, Kaiyu, 2017. "A novel stochastic modeling method to simulate cooling loads in residential districts," Applied Energy, Elsevier, vol. 206(C), pages 134-149.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    5. Pachauri, Nikhil & Ahn, Chang Wook, 2023. "Weighted aggregated ensemble model for energy demand management of buildings," Energy, Elsevier, vol. 263(PC).
    6. 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).
    7. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saeid Charani Shandiz & Alice Denarie & Gabriele Cassetti & Marco Calderoni & Antoine Frein & Mario Motta, 2019. "A Simplified Methodology for Existing Tertiary Buildings’ Cooling Energy Need Estimation at District Level: A Feasibility Study of a District Cooling System in Marrakech," Energies, MDPI, vol. 12(5), pages 1-20, March.
    2. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    3. Zinzi, Michele & Carnielo, Emiliano & Mattoni, Benedetta, 2018. "On the relation between urban climate and energy performance of buildings. A three-years experience in Rome, Italy," Applied Energy, Elsevier, vol. 221(C), pages 148-160.
    4. Andrea Menapace & Simone Santopietro & Rudy Gargano & Maurizio Righetti, 2021. "Stochastic Generation of District Heat Load," Energies, MDPI, vol. 14(17), pages 1-17, August.
    5. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    7. Marzullo, Thibault & Keane, Marcus M. & Geron, Marco & Monaghan, Rory F.D., 2019. "A computational toolchain for the automatic generation of multiple Reduced-Order Models from CFD simulations," Energy, Elsevier, vol. 180(C), pages 511-519.
    8. Barth, Florian & Schüppler, Simon & Menberg, Kathrin & Blum, Philipp, 2023. "Estimating cooling capacities from aerial images using convolutional neural networks," Applied Energy, Elsevier, vol. 349(C).
    9. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    10. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.
    11. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    12. Selva Calixto & Marco Cozzini & Giampaolo Manzolini, 2021. "Modelling of an Existing Neutral Temperature District Heating Network: Detailed and Approximate Approaches," Energies, MDPI, vol. 14(2), pages 1-16, January.
    13. Mei, Fei & Zhang, Jiatang & Lu, Jixiang & Lu, Jinjun & Jiang, Yuhan & Gu, Jiaqi & Yu, Kun & Gan, Lei, 2021. "Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations," Energy, Elsevier, vol. 219(C).
    14. Ma, Weiwu & Fang, Song & Liu, Gang, 2017. "Hybrid optimization method and seasonal operation strategy for distributed energy system integrating CCHP, photovoltaic and ground source heat pump," Energy, Elsevier, vol. 141(C), pages 1439-1455.
    15. Tiziano Dalla Mora & Lorenzo Teso & Laura Carnieletto & Angelo Zarrella & Piercarlo Romagnoni, 2021. "Comparative Analysis between Dynamic and Quasi-Steady-State Methods at an Urban Scale on a Social-Housing District in Venice," Energies, MDPI, vol. 14(16), pages 1-22, August.
    16. Zhang, Xingxing & Lovati, Marco & Vigna, Ilaria & Widén, Joakim & Han, Mengjie & Gal, Csilla & Feng, Tao, 2018. "A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions," Applied Energy, Elsevier, vol. 230(C), pages 1034-1056.
    17. Mastrucci, Alessio & Marvuglia, Antonino & Benetto, Enrico & Leopold, Ulrich, 2020. "A spatio-temporal life cycle assessment framework for building renovation scenarios at the urban scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).
    18. Rodrigues, Eugénio & Fernandes, Marco S. & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Costa, José J., 2019. "Performance-based design of multi-story buildings for a sustainable urban environment: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    19. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    20. Aldubyan, Mohammad & Krarti, Moncef, 2022. "Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study," Energy, Elsevier, vol. 238(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:238:y:2019:i:c:p:963-971. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.