IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v199y2020ics0360544220305612.html
   My bibliography  Save this article

Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system

Author

Listed:
  • Yuan, Jianjuan
  • Zhou, Zhihua
  • Tang, Huajie
  • Wang, Chendong
  • Lu, Shilei
  • Han, Zhao
  • Zhang, Ji
  • Sheng, Ying

Abstract

In-depth knowledge of the customers and a better understanding of their heat use is the basis of accurate heating load prediction model, it is also the cornerstone for realizing the intelligent and multi-energy development of the heating system. Most of existing heating load prediction models were established mainly based on outdoor meteorological parameter, while few of them took indoor temperature and user behavior into account. This paper took the wireless on-off control system as an example, and the heat user behavior was analyzed by the open valve number. Different heat users have different heat behaviors in different time periods, which are reflected in the change of open valve number. The number of open valve was first time introduced into the heating load prediction model (Model-2) in this paper. The results showed that the prediction accuracy of Model-2 was significantly improved compared with the traditional model (Model-1) including multiple linear regression (MLR) and support vector machine (SVM). K-means algorithm was used to identify and cluster the heat user behavior. Comparing the heating load prediction accuracy of MLR and SVM before and after clustering (Model-2 and Model-3), the results showed that Model-3 had a much higher accuracy. The rank of the models was Model-3, Model-2 and Model-1 in turn, in terms of the performance coefficients for model training and testing. The Model-3 proposed in this paper provides a reliable heating load prediction method for wireless on-off control system.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:199:y:2020:i:c:s0360544220305612
    DOI: 10.1016/j.energy.2020.117454
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220305612
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117454?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. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
    2. 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).
    3. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio Sánchez-Esguevillas, 2012. "Classification and Clustering of Electricity Demand Patterns in Industrial Parks," Energies, MDPI, vol. 5(12), pages 1-14, December.
    4. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    5. Gu, Jihao & Wang, Jin & Qi, Chengying & Min, Chunhua & Sundén, Bengt, 2018. "Medium-term heat load prediction for an existing residential building based on a wireless on-off control system," Energy, Elsevier, vol. 152(C), pages 709-718.
    6. Calikus, Ece & Nowaczyk, Sławomir & Sant'Anna, Anita & Gadd, Henrik & Werner, Sven, 2019. "A data-driven approach for discovering heat load patterns in district heating," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    7. Münster, Marie & Morthorst, Poul Erik & Larsen, Helge V. & Bregnbæk, Lars & Werling, Jesper & Lindboe, Hans Henrik & Ravn, Hans, 2012. "The role of district heating in the future Danish energy system," Energy, Elsevier, vol. 48(1), pages 47-55.
    8. Herui Cui & Pengbang Wei & Yupei Mu & Xu Peng, 2016. "SARIMA-Orthogonal Polynomial Curve Fitting Model for Medium-Term Load Forecasting," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-9, October.
    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. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    11. Gadd, Henrik & Werner, Sven, 2014. "Achieving low return temperatures from district heating substations," Applied Energy, Elsevier, vol. 136(C), pages 59-67.
    12. Goia, Aldo & May, Caterina & Fusai, Gianluca, 2010. "Functional clustering and linear regression for peak load forecasting," International Journal of Forecasting, Elsevier, vol. 26(4), pages 700-711, October.
    13. Yetemen, Omer & Yalcin, Tolga, 2009. "Climatic parameters and evaluation of energy consumption of the Afyon geothermal district heating system, Afyon, Turkey," Renewable Energy, Elsevier, vol. 34(3), pages 706-710.
    14. Werner, Sven, 2017. "International review of district heating and cooling," Energy, Elsevier, vol. 137(C), pages 617-631.
    15. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    16. Rezaie, Behnaz & Rosen, Marc A., 2012. "District heating and cooling: Review of technology and potential enhancements," Applied Energy, Elsevier, vol. 93(C), pages 2-10.
    17. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
    18. Guo, Xiaofeng & Hendel, Martin, 2018. "Urban water networks as an alternative source for district heating and emergency heat-wave cooling," Energy, Elsevier, vol. 145(C), pages 79-87.
    19. Alkan, Mehmet Ali & Keçebaş, Ali & Yamankaradeniz, Nurettin, 2013. "Exergoeconomic analysis of a district heating system for geothermal energy using specific exergy cost method," Energy, Elsevier, vol. 60(C), pages 426-434.
    20. 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.
    21. 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.
    22. Shamshirband, Shahaboddin & Petković, Dalibor & Enayatifar, Rasul & Hanan Abdullah, Abdul & Marković, Dušan & Lee, Malrey & Ahmad, Rodina, 2015. "Heat load prediction in district heating systems with adaptive neuro-fuzzy method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 760-767.
    23. Liao, Chunhui & Ertesvåg, Ivar S. & Zhao, Jianing, 2013. "Energetic and exergetic efficiencies of coal-fired CHP (combined heat and power) plants used in district heating systems of China," Energy, Elsevier, vol. 57(C), pages 671-681.
    24. Rahman, Aowabin & Smith, Amanda D., 2018. "Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms," Applied Energy, Elsevier, vol. 228(C), pages 108-121.
    25. Lund, H. & Möller, B. & Mathiesen, B.V. & Dyrelund, A., 2010. "The role of district heating in future renewable energy systems," Energy, Elsevier, vol. 35(3), pages 1381-1390.
    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. Huang, Ke & Lu, Shilei & Han, Zhao & Yuan, Jianjuan, 2023. "Research on heat consumption detection, restoration and prediction methods for discontinuous heating substation," Energy, Elsevier, vol. 266(C).
    2. Anh Tuan Phan & Thi Tuyet Hong Vu & Dinh Quang Nguyen & Eleonora Riva Sanseverino & Hang Thi-Thuy Le & Van Cong Bui, 2022. "Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network," Energies, MDPI, vol. 15(23), pages 1-16, December.
    3. 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).
    4. Wang, Zhijin & Liu, Xiufeng & Huang, Yaohui & Zhang, Peisong & Fu, Yonggang, 2023. "A multivariate time series graph neural network for district heat load forecasting," Energy, Elsevier, vol. 278(PA).
    5. 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).
    6. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    7. Wang, Chendong & Yuan, Jianjuan & Huang, Ke & Zhang, Ji & Zheng, Lihong & Zhou, Zhihua & Zhang, Yufeng, 2022. "Research on thermal load prediction of district heating station based on transfer learning," Energy, Elsevier, vol. 239(PE).
    8. Zhang, Xuemei & Yuan, Jianjuan & Kong, Xiangfei & Han, Jingxiao & Shi, Ying, 2023. "Coupling of flexible phase change materials and pipe for improving the stability of heating system," Energy, Elsevier, vol. 275(C).
    9. Mengyao Lu & Guitao Xu & Jianjuan Yuan, 2023. "Installation Principle and Calculation Model of the Representative Indoor Temperature-Monitoring Points in Large-Scale Buildings," Energies, MDPI, vol. 16(17), pages 1-19, September.
    10. 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).

    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. 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).
    2. 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).
    3. 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).
    4. 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).
    5. Wang, Chendong & Yuan, Jianjuan & Zhang, Ji & Deng, Na & Zhou, Zhihua & Gao, Feng, 2020. "Multi-criteria comprehensive study on predictive algorithm of heating energy consumption of district heating station based on timeseries processing," Energy, Elsevier, vol. 202(C).
    6. 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).
    7. Lumbreras, Mikel & Garay-Martinez, Roberto & Arregi, Beñat & Martin-Escudero, Koldobika & Diarce, Gonzalo & Raud, Margus & Hagu, Indrek, 2022. "Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters," Energy, Elsevier, vol. 239(PD).
    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. Calikus, Ece & Nowaczyk, Sławomir & Sant'Anna, Anita & Gadd, Henrik & Werner, Sven, 2019. "A data-driven approach for discovering heat load patterns in district heating," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    10. 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).
    11. Wang, Yanmin & Li, Zhiwei & Liu, Junjie & Pei, Mingzhe & Zhao, Yan & Lu, Xuan, 2023. "Data-driven analysis and prediction of indoor characteristic temperature in district heating systems," Energy, Elsevier, vol. 282(C).
    12. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    13. 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).
    14. Guelpa, Elisa & Marincioni, Ludovica & Verda, Vittorio, 2019. "Towards 4th generation district heating: Prediction of building thermal load for optimal management," Energy, Elsevier, vol. 171(C), pages 510-522.
    15. Lake, Andrew & Rezaie, Behanz & Beyerlein, Steven, 2017. "Review of district heating and cooling systems for a sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 417-425.
    16. Guelpa, Elisa & Verda, Vittorio, 2020. "Automatic fouling detection in district heating substations: Methodology and tests," Applied Energy, Elsevier, vol. 258(C).
    17. 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).
    18. 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.
    19. 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).
    20. F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2020. "Joint and conditional dependence modelling of peak district heating demand and outdoor temperature: a copula-based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 373-395, June.

    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:199:y:2020:i:c:s0360544220305612. 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.