IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v278y2023ics0360544223010319.html
   My bibliography  Save this article

Load forecasting of district heating system based on improved FB-Prophet model

Author

Listed:
  • Shakeel, Asim
  • Chong, Daotong
  • Wang, Jinshi

Abstract

Accurate load forecasting of the district heating network (DHN) is essential to guarantee effective energy production, distribution, and rational utilization. An improved Facebook-Prophet (FB-Prophet) model with additional positional encoding layers has been developed to forecast the DHN heat consumption. The accuracy of univariate and multivariate FB-Prophet models is evaluated; this paper also evaluates the optimum training dataset length. To explore the performance of the improved FB-Prophet model in heating load forecasting tasks, another seven machine learning models, namely FB-Prophet, DeepVAR, long-short term memory, extreme gradient boosting, multilayer perceptron, recurrent neural network, and support vector regression are used for comparison. The historical heating load, outside temperature, relative humidity, speed of wind, direction of wind, and weather type of a DHN in Serbia are utilized to extensively investigate the effectiveness of the improved FB-Prophet model. The prediction outcomes of all the models are thoroughly analyzed. The results indicate that the improved FB-Prophet model can generate the most precise and consistent predictions and it showed better results for sparse DHN data. The prediction curve is fitted to the trend of hourly DHN consumption change, which can play an effective guiding function in the distribution of heat.

Suggested Citation

  • Shakeel, Asim & Chong, Daotong & Wang, Jinshi, 2023. "Load forecasting of district heating system based on improved FB-Prophet model," Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:c:s0360544223010319
    DOI: 10.1016/j.energy.2023.127637
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223010319
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127637?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. Suqi Zhang & Ningjing Zhang & Ziqi Zhang & Ying Chen, 2022. "Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm," Energies, MDPI, vol. 15(23), pages 1-17, December.
    2. Lund, Henrik & Werner, Sven & Wiltshire, Robin & Svendsen, Svend & Thorsen, Jan Eric & Hvelplund, Frede & Mathiesen, Brian Vad, 2014. "4th Generation District Heating (4GDH)," Energy, Elsevier, vol. 68(C), pages 1-11.
    3. Izadyar, Nima & Ghadamian, Hossein & Ong, Hwai Chyuan & moghadam, Zeinab & Tong, Chong Wen & Shamshirband, Shahaboddin, 2015. "Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption," Energy, Elsevier, vol. 93(P2), pages 1558-1567.
    4. Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
    5. Holmgren, Kristina, 2006. "Role of a district-heating network as a user of waste-heat supply from various sources - the case of Göteborg," Applied Energy, Elsevier, vol. 83(12), pages 1351-1367, December.
    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. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "District heating load patterns and short-term forecasting for buildings and city level," Energy, Elsevier, vol. 289(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. Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
    2. Simon Moser & Stefan Puschnigg, 2021. "Supra-Regional District Heating Networks: A Missing Infrastructure for a Sustainable Energy System," Energies, MDPI, vol. 14(12), pages 1-15, June.
    3. Olsson, Linda & Wetterlund, Elisabeth & Söderström, Mats, 2015. "Assessing the climate impact of district heating systems with combined heat and power production and industrial excess heat," Resources, Conservation & Recycling, Elsevier, vol. 96(C), pages 31-39.
    4. Pereverza, Kateryna & Pasichnyi, Oleksii & Lazarevic, David & Kordas, Olga, 2017. "Strategic planning for sustainable heating in cities: A morphological method for scenario development and selection," Applied Energy, Elsevier, vol. 186(P2), pages 115-125.
    5. Guelpa, Elisa & Verda, Vittorio, 2020. "Automatic fouling detection in district heating substations: Methodology and tests," Applied Energy, Elsevier, vol. 258(C).
    6. Popovski, Eftim & Fleiter, Tobias & Santos, Hugo & Leal, Vitor & Fernandes, Eduardo Oliveira, 2018. "Technical and economic feasibility of sustainable heating and cooling supply options in southern European municipalities-A case study for Matosinhos, Portugal," Energy, Elsevier, vol. 153(C), pages 311-323.
    7. Jie, Pengfei & Kong, Xiangfei & Rong, Xian & Xie, Shangqun, 2016. "Selecting the optimum pressure drop per unit length of district heating piping network based on operating strategies," Applied Energy, Elsevier, vol. 177(C), pages 341-353.
    8. Li, Haoran & Hou, Juan & Hong, Tianzhen & Ding, Yuemin & Nord, Natasa, 2021. "Energy, economic, and environmental analysis of integration of thermal energy storage into district heating systems using waste heat from data centres," Energy, Elsevier, vol. 219(C).
    9. Karner, Katharina & Theissing, Matthias & Kienberger, Thomas, 2017. "Modeling of energy efficiency increase of urban areas through synergies with industries," Energy, Elsevier, vol. 136(C), pages 201-209.
    10. Persson, U. & Möller, B. & Werner, S., 2014. "Heat Roadmap Europe: Identifying strategic heat synergy regions," Energy Policy, Elsevier, vol. 74(C), pages 663-681.
    11. Pieper, Henrik & Ommen, Torben & Elmegaard, Brian & Brix Markussen, Wiebke, 2019. "Assessment of a combination of three heat sources for heat pumps to supply district heating," Energy, Elsevier, vol. 176(C), pages 156-170.
    12. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    13. Rismanchi, B., 2017. "District energy network (DEN), current global status and future development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 571-579.
    14. Zhao, Yin & Gong, Mingju & Sun, Jiawang & Han, Cuitian & Jing, Lei & Li, Bo & Zhao, Zhixuan, 2023. "A new hybrid optimization prediction strategy based on SH-Informer for district heating system," Energy, Elsevier, vol. 282(C).
    15. Moser, Simon & Puschnigg, Stefan & Rodin, Valerie, 2020. "Designing the Heat Merit Order to determine the value of industrial waste heat for district heating systems," Energy, Elsevier, vol. 200(C).
    16. Guelpa, Elisa & Verda, Vittorio, 2021. "Demand response and other demand side management techniques for district heating: A review," Energy, Elsevier, vol. 219(C).
    17. Zheng, Xuejing & Shi, Zhiyuan & Wang, Yaran & Zhang, Huan & Tang, Zhiyun, 2024. "Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction," Energy, Elsevier, vol. 288(C).
    18. Yuan, Jianjuan & Zhou, Zhihua & Tang, Huajie & Wang, Chendong & Lu, Shilei & Han, Zhao & Zhang, Ji & Sheng, Ying, 2020. "Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system," Energy, Elsevier, vol. 199(C).
    19. 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).
    20. 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).

    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:278:y:2023:i:c:s0360544223010319. 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.