IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipcs0306261924020026.html
   My bibliography  Save this article

A day-ahead operational regulation method for solar district heating systems based on model predictive control

Author

Listed:
  • Xin, Xin
  • Liu, Yanfeng
  • Zhang, Zhihao
  • Zheng, Huifan
  • Zhou, Yong

Abstract

Solar district heating systems are widely used in solar-rich areas due to their centralized management and ease of maintenance. However, traditional temperature difference-based control methods do not consider the various dynamic factors affecting collector array efficiency, resulting in sub-optimal heat collection and storage. Additionally, traditional heating control methods do not consider the relationship between heat storage and demand, and district heating systems have delayed response times. This increases auxiliary heating output, leading to unstable heating stability and indoor temperature. To address these issues, this paper proposes a model predictive control (MPC)-based day-ahead operation regulation method for solar district heating systems. A sequence-to-sequence long short-term memory (Seq2seq-LSTM) prediction model is developed to forecast outdoor environmental parameters and building heating loads for the next 24 h. This prediction model is combined with the dynamic operation control model of the solar district heating systems to develop an MPC-based day-ahead operation regulation model. Simulation results show that compared to rule-based control (RBC), MPC can dynamically identify optimal control points in real time, keeping the collector array within a high-efficiency operation range. Consequently, heat collection increased by 5.4 %, and the solar fraction increased by 9.1 %. MPC can balance heat storage with end-use heat demand, achieving more efficient heating by reasonably reducing the average water tank temperature. With MPC, the average water tank temperature decreased by 1.52 °C compared to RBC, and heat loss decreased by 3.2 %. MPC fully considers the time lag characteristics on the building side, reducing temperature fluctuations through day-ahead adjustments. The average supply-return water temperature difference with MPC decreased by 3.19 °C compared to RBC. The average indoor temperatures for the four types of buildings with MPC were 18.11 °C, 18.06 °C, 18.26 °C, and 18.12 °C, respectively, closer to the pre-set temperature of 18 °C. Finally, the total energy consumption of the system decreased by 26.5 % with MPC, including a 27.14 % reduction in auxiliary heat source energy consumption. In summary, MPC significantly improves energy efficiency, stability, and energy savings.

Suggested Citation

  • Xin, Xin & Liu, Yanfeng & Zhang, Zhihao & Zheng, Huifan & Zhou, Yong, 2025. "A day-ahead operational regulation method for solar district heating systems based on model predictive control," Applied Energy, Elsevier, vol. 377(PC).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pc:s0306261924020026
    DOI: 10.1016/j.apenergy.2024.124619
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924020026
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124619?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. Sun, Guoxin & Yu, Yongheng & Yu, Qihui & Tan, Xin & Wu, Linfeng & Wang, Yahui, 2024. "Enhancing control and performance evaluation of composite heating systems through modal analysis and model predictive control: Design and comprehensive analysis," Applied Energy, Elsevier, vol. 357(C).
    2. Frison, Lilli & Kollmar, Manuel & Oliva, Axel & Bürger, Adrian & Diehl, Moritz, 2024. "Model predictive control of bidirectional heat transfer in prosumer-based solar district heating networks," Applied Energy, Elsevier, vol. 358(C).
    3. Pawel Znaczko & Kazimierz Kaminski & Norbert Chamier-Gliszczynski & Emilian Szczepanski & Paweł Gołda, 2021. "Experimental Analysis of Control Methods in Solar Water Heating Systems," Energies, MDPI, vol. 14(24), pages 1-16, December.
    4. Wei, Zhichen & Calautit, John, 2023. "Predictive control of low-temperature heating system with passive thermal mass energy storage and photovoltaic system: Impact of occupancy patterns and climate change," Energy, Elsevier, vol. 269(C).
    5. García, Jesús & Barraza, Rodrigo & Soo Too, Yen Chean & Vásquez-Padilla, Ricardo & Acosta, David & Estay, Danilo & Valdivia, Patricio, 2022. "Transient simulation of a control strategy for solar receivers based on mass flow valves adjustments and heliostats aiming," Renewable Energy, Elsevier, vol. 185(C), pages 1221-1244.
    6. Brown, Sarah & Beausoleil-Morrison, Ian, 2023. "Long-term implementation of a model predictive controller for a hydronic floor heating and cooling system in a highly glazed house in Canada," Applied Energy, Elsevier, vol. 349(C).
    7. Velarde, Pablo & Gallego, Antonio J. & Bordons, Carlos & Camacho, Eduardo F., 2023. "Scenario-based model predictive control for energy scheduling in a parabolic trough concentrating solar plant with thermal storage," Renewable Energy, Elsevier, vol. 206(C), pages 1228-1238.
    8. Novak, Milan & Vohnout, Rudolf & Landkamer, Ladislav & Budik, Ondrej & Eider, Markus & Mukherjee, Amrit, 2023. "Energy-efficient smart solar system cooling for real-time dynamic weather changes in mild-climate regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    9. Wang, Jiaxing & Li, Yiguo & Zhang, Junli, 2023. "Coordinated control of concentrated solar power systems with indirect molten salt storage considering operation mode switching: Using switching model predictive control," Energy, Elsevier, vol. 268(C).
    10. Gholamibozanjani, Gohar & Tarragona, Joan & Gracia, Alvaro de & Fernández, Cèsar & Cabeza, Luisa F. & Farid, Mohammed M., 2018. "Model predictive control strategy applied to different types of building for space heating," Applied Energy, Elsevier, vol. 231(C), pages 959-971.
    11. Anderson, John E. & Wulfhorst, Gebhard & Lang, Werner, 2015. "Energy analysis of the built environment—A review and outlook," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 149-158.
    12. Ahmad, Ashfaq & Khan, Jamil Yusuf, 2020. "Real-Time Load Scheduling, Energy Storage Control and Comfort Management for Grid-Connected Solar Integrated Smart Buildings," Applied Energy, Elsevier, vol. 259(C).
    13. 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).
    14. Karamali, Mohammad & Khodabandeh, Mahdi, 2017. "A distributed solar collector field temperature profile control and estimation using inlet oil temperature and radiation estimates based on Iterative Extended Kalman Filter," Renewable Energy, Elsevier, vol. 101(C), pages 144-155.
    15. Wang, Lu & Yuan, JianJuan & Qiao, Xu & Kong, Xiangfei, 2023. "Optimal rule based double predictive control for the management of thermal energy in a distributed clean heating system," Renewable Energy, Elsevier, vol. 215(C).
    16. Ciampi, Giovanni & Rosato, Antonio & Sibilio, Sergio, 2018. "Thermo-economic sensitivity analysis by dynamic simulations of a small Italian solar district heating system with a seasonal borehole thermal energy storage," Energy, Elsevier, vol. 143(C), pages 757-771.
    17. Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
    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. Abuelhamd, Muhammad & Cañizares, Claudio A., 2024. "Dynamic model of integrated electricity and district heating for remote communities," Applied Energy, Elsevier, vol. 376(PB).
    2. Maragna, Charles & Rey, Charlotte & Perreaux, Marc, 2023. "A novel and versatile solar Borehole Thermal Energy Storage assisted by a Heat Pump. Part 1: System description," Renewable Energy, Elsevier, vol. 208(C), pages 709-725.
    3. 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).
    4. Pokhrel, Sajjan & Amiri, Leyla & Zueter, Ahmad & Poncet, Sébastien & Hassani, Ferri P. & Sasmito, Agus P. & Ghoreishi-Madiseh, Seyed Ali, 2021. "Thermal performance evaluation of integrated solar-geothermal system; a semi-conjugate reduced order numerical model," Applied Energy, Elsevier, vol. 303(C).
    5. Gholamibozanjani, Gohar & Farid, Mohammed, 2020. "A comparison between passive and active PCM systems applied to buildings," Renewable Energy, Elsevier, vol. 162(C), pages 112-123.
    6. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    7. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    8. Antonio Rosato & Antonio Ciervo & Giovanni Ciampi & Michelangelo Scorpio & Sergio Sibilio, 2020. "Integration of Micro-Cogeneration Units and Electric Storages into a Micro-Scale Residential Solar District Heating System Operating with a Seasonal Thermal Storage," Energies, MDPI, vol. 13(20), pages 1-40, October.
    9. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Xiaoxia Li & Husheng Qiu & Zhifeng Wang & Jinping Li & Guobin Yuan & Xiao Guo & Lifeng Jin, 2023. "Numerical Investigation of a Solar-Heating System with Solar-Tower Receiver and Seasonal Storage in Northern China: Dynamic Performance Assessment and Operation Strategy Analysis," Energies, MDPI, vol. 16(14), pages 1-27, July.
    11. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    12. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    13. Lee, Junghun & Kim, Jeonggook & Song, Doosam & Kim, Jonghun & Jang, Cheolyong, 2017. "Impact of external insulation and internal thermal density upon energy consumption of buildings in a temperate climate with four distinct seasons," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1081-1088.
    14. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    15. Pérez-Sánchez, Laura À. & Velasco-Fernández, Raúl & Giampietro, Mario, 2022. "Factors and actions for the sustainability of the residential sector. The nexus of energy, materials, space, and time use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    16. Ajith, Meenu & Martínez-Ramón, Manel, 2021. "Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data," Applied Energy, Elsevier, vol. 294(C).
    17. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).
    18. 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).
    19. Ilze Polikarpova & Roberts Kakis & Ieva Pakere & Dagnija Blumberga, 2021. "Optimizing Large-Scale Solar Field Efficiency: Latvia Case Study," Energies, MDPI, vol. 14(14), pages 1-13, July.
    20. Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.

    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:377:y:2025:i:pc:s0306261924020026. 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.