IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v294y2024ics0360544224006674.html
   My bibliography  Save this article

Evaluation of the controllability of multi-family building with radiator heating systems: A frequency domain approach

Author

Listed:
  • Liu, Zhikai
  • Zhang, Huan
  • Wang, Yaran
  • You, Shijun
  • Dai, Ting
  • Jiang, Yan

Abstract

For multi-family buildings with radiator heating system (MFBRHS) in China, on-demand heating is an important route to achieve energy saving, emission reduction, and improve user satisfaction. The current researchs are mainly focused on load forecasting and achieving heat balance by adjusting supply temperature or flow rate. However, the time-delay of disturbances factors and control parameters caused by the building envelopes and hydraulic coupling of the network is often ignored, which results in the failure to truly achieve heat balance. In this paper, a linearization method is proposed for the MFBRHS, and the frequency domain approach was employed as an analytical tool to gain a deeper understanding of the dynamic characteristics of the system. The results show that the selected building is equivalent to a low-pass filter. The average response speed of supply temperature and flow rate is slower than that of the solar radiation and outdoor temperature, which indicates that each control parameter should be delayed for adjustment to truly achieve heat balance. Additionally, the uncertainty of the building envelope only affects the magnitude of the disturbances while has a limited effect on its response speed.

Suggested Citation

  • Liu, Zhikai & Zhang, Huan & Wang, Yaran & You, Shijun & Dai, Ting & Jiang, Yan, 2024. "Evaluation of the controllability of multi-family building with radiator heating systems: A frequency domain approach," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006674
    DOI: 10.1016/j.energy.2024.130895
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224006674
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130895?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. Gustafsson, Jonas & Delsing, Jerker & van Deventer, Jan, 2010. "Improved district heating substation efficiency with a new control strategy," Applied Energy, Elsevier, vol. 87(6), pages 1996-2004, June.
    2. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    3. Sha, Le & Jiang, Ziwei & Sun, Hejiang, 2023. "A control strategy of heating system based on adaptive model predictive control," Energy, Elsevier, vol. 273(C).
    4. Aoun, Nadine & Bavière, Roland & Vallée, Mathieu & Aurousseau, Antoine & Sandou, Guillaume, 2019. "Modelling and flexible predictive control of buildings space-heating demand in district heating systems," Energy, Elsevier, vol. 188(C).
    5. Liu, Zhikai & Zhang, Huang & Wang, Yaran & Fan, Xianwang & You, Shijun & Li, Ang, 2023. "Data-driven predictive model for feedback control of supply temperature in buildings with radiator heating system," Energy, Elsevier, vol. 280(C).
    6. Alimonti, C. & Conti, P. & Soldo, E., 2021. "Producing geothermal energy with a deep borehole heat exchanger: Exergy optimization of different applications and preliminary design criteria," Energy, Elsevier, vol. 220(C).
    7. Wang, Yaran & You, Shijun & Zhang, Huan & Zheng, Wandong & Zheng, Xuejing & Miao, Qingwei, 2017. "Hydraulic performance optimization of meshed district heating network with multiple heat sources," Energy, Elsevier, vol. 126(C), pages 603-621.
    8. Xue, Puning & Jiang, Yi & Zhou, Zhigang & Chen, Xin & Fang, Xiumu & Liu, Jing, 2019. "Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms," Energy, Elsevier, vol. 188(C).
    9. Yuan, Jianjuan & Wang, Chendong & Zhou, Zhihua, 2019. "Study on refined control and prediction model of district heating station based on support vector machine," Energy, Elsevier, vol. 189(C).
    10. Zhong, Wei & Feng, Encheng & Lin, Xiaojie & Xie, Jinfang, 2022. "Research on data-driven operation control of secondary loop of district heating system," Energy, Elsevier, vol. 239(PB).
    11. Liu, Zhikai & Zhang, Huan & Wang, Yaran & Jiang, Yan & He, Zhihao & Zhou, Pengkun, 2023. "An adaptive double-Newton-iteration hydraulic calculation method for optimal operation of the meshed district heating network," Energy, Elsevier, vol. 272(C).
    12. Xue, Guixiang & Qi, Chengying & Li, Han & Kong, Xiangfei & Song, Jiancai, 2020. "Heating load prediction based on attention long short term memory: A case study of Xingtai," Energy, Elsevier, vol. 203(C).
    13. Zhang, Lipeng & Gudmundsson, Oddgeir & Thorsen, Jan Eric & Li, Hongwei & Li, Xiaopeng & Svendsen, Svend, 2016. "Method for reducing excess heat supply experienced in typical Chinese district heating systems by achieving hydraulic balance and improving indoor air temperature control at the building level," Energy, Elsevier, vol. 107(C), pages 431-442.
    14. Shah, Sheikh Khaleduzzaman & Aye, Lu & Rismanchi, Behzad, 2020. "Multi-objective optimisation of a seasonal solar thermal energy storage system for space heating in cold climate," Applied Energy, Elsevier, vol. 268(C).
    15. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).
    16. Nie, Yazhou & Deng, Mengsi & Shan, Ming & Yang, Xudong, 2023. "Clean and low-carbon heating in the building sector of China: 10-Year development review and policy implications," Energy Policy, Elsevier, vol. 179(C).
    17. Sun, Chunhua & Chen, Jiali & Cao, Shanshan & Gao, Xiaoyu & Xia, Guoqiang & Qi, Chengying & Wu, Xiangdong, 2021. "A dynamic control strategy of district heating substations based on online prediction and indoor temperature feedback," Energy, Elsevier, vol. 235(C).
    18. Liu, Zhikai & Zhang, Huan & Wang, Yaran & Song, Zixu & You, Shijun & Jiang, Yan & Wu, Zhangxiang, 2022. "A thermal-hydraulic coupled simulation approach for the temperature and flow rate control strategy evaluation of the multi-room radiator heating system," Energy, Elsevier, vol. 246(C).
    Full references (including those not matched with items on IDEAS)

    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. Liu, Zhikai & Zhang, Huang & Wang, Yaran & Fan, Xianwang & You, Shijun & Li, Ang, 2023. "Data-driven predictive model for feedback control of supply temperature in buildings with radiator heating system," Energy, Elsevier, vol. 280(C).
    2. Liu, Zhikai & Zhang, Huan & Wang, Yaran & Song, Zixu & You, Shijun & Jiang, Yan & Wu, Zhangxiang, 2022. "A thermal-hydraulic coupled simulation approach for the temperature and flow rate control strategy evaluation of the multi-room radiator heating system," Energy, Elsevier, vol. 246(C).
    3. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    4. Zhong, Wei & Feng, Encheng & Lin, Xiaojie & Xie, Jinfang, 2022. "Research on data-driven operation control of secondary loop of district heating system," Energy, Elsevier, vol. 239(PB).
    5. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort," Energy, Elsevier, vol. 289(C).
    6. Liu, Zhikai & Zhang, Huan & Wang, Yaran & Fan, Xianwang & You, Shijun & Jiang, Yan & Gao, Xinlei, 2023. "Optimization of hydraulic distribution using loop adjustment method in meshed district heating system with multiple heat sources," Energy, Elsevier, vol. 284(C).
    7. Wang, Yang & Zhang, Shanhong & Chow, David & Kuckelkorn, Jens M., 2021. "Evaluation and optimization of district energy network performance: Present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    8. Huang, Ke & Yuan, Jianjuan & Zhou, Zhihua & Zheng, Xuejing, 2022. "Analysis and evaluation of heat source data of large-scale heating system based on descriptive data mining techniques," Energy, Elsevier, vol. 251(C).
    9. Yuan, Jianjuan & Huang, Ke & Lu, Shilei & Zhang, Ji & Han, Zhao & Zhou, Zhihua, 2022. "Analysis of influencing factors on heat consumption of large residential buildings with different occupancy rates-Tianjin case study," Energy, Elsevier, vol. 238(PC).
    10. Yuan, Jianjuan & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei & Zhou, Zhihua, 2022. "Evaluation of the operation data for improving the prediction accuracy of heating parameters in heating substation," Energy, Elsevier, vol. 238(PB).
    11. Sun, Chunhua & Liu, Yanan & Gao, Xiaoyu & Wang, Jinda & Yang, Lan & Qi, Chengyong, 2022. "Research on control strategy integrated with characteristics of user's energy-saving behavior of district heating system," Energy, Elsevier, vol. 245(C).
    12. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    13. Morovat, Navid & Athienitis, Andreas K. & Candanedo, José Agustín & Nouanegue, Hervé Frank, 2024. "Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building," Energy, Elsevier, vol. 296(C).
    14. Sun, Chunhua & Liu, Yiting & Cao, Shanshan & Chen, Jiali & Xia, Guoqiang & Wu, Xiangdong, 2022. "Identification of control regularity of heating stations based on cross-correlation function dynamic time delay method," Energy, Elsevier, vol. 246(C).
    15. Yuan, Jianjuan & Huang, Ke & Han, Zhao & Zhou, Zhihua & Lu, Shilei, 2021. "A new feedback predictive model for improving the operation efficiency of heating station based on indoor temperature," Energy, Elsevier, vol. 222(C).
    16. Runge, Jason & Saloux, Etienne, 2023. "A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system," Energy, Elsevier, vol. 269(C).
    17. Yuan, Jianjuan & Zhou, Zhihua & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei, 2021. "Analysis and evaluation of the operation data for achieving an on-demand heating consumption prediction model of district heating substation," Energy, Elsevier, vol. 214(C).
    18. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    19. Saloux, Etienne & Candanedo, José A., 2021. "Model-based predictive control to minimize primary energy use in a solar district heating system with seasonal thermal energy storage," Applied Energy, Elsevier, vol. 291(C).
    20. Licklederer, Thomas & Zinsmeister, Daniel & Lukas, Lorenz & Speer, Fabian & Hamacher, Thomas & Perić, Vedran S., 2024. "Control of bidirectional prosumer substations in smart thermal grids: A weighted proportional-integral control approach," Applied Energy, Elsevier, vol. 354(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:energy:v:294:y:2024:i:c:s0360544224006674. 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.journals.elsevier.com/energy .

    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.