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Data-driven predictive model for feedback control of supply temperature in buildings with radiator heating system

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  • Liu, Zhikai
  • Zhang, Huang
  • Wang, Yaran
  • Fan, Xianwang
  • You, Shijun
  • Li, Ang

Abstract

For residential buildings with radiator heating system, the supply temperature is conventionally adjusted by weather compensator, which results in large fluctuations in the indoor air temperature and high energy consumption of heating. The solutions on current researches mainly focus on heat prediction of demand side, so that the supply temperature can be adjusted according to the predicted heat demand and achieve a heat balance. However, this requires a significant amount of historical data, which limits their applicability. And the lack of pre-simulation platform of the new strategies makes their practical application more difficult. In this paper, a digital twinning of secondary district heating system (DHS) is created, which considers both human behavior and radiator heat dissipation characteristics. A new supply temperature regulate strategy based on indoor temperature feedback is proposed. It contains an indoor temperature prediction model, which require a small amount of measured data and can keep the prediction error within ±0.6 °C. The control effects are analyzed by comparing the proposed regulation strategy with the weather compensator. The result shows that the new temperature regulate strategy is superior to the weather compensator in maintaining indoor temperature and can save energy by 5.4%.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223016420
    DOI: 10.1016/j.energy.2023.128248
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    References listed on IDEAS

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    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. 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.
    3. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    4. Jangsten, M. & Kensby, J. & Dalenbäck, J.-O. & Trüschel, A., 2017. "Survey of radiator temperatures in buildings supplied by district heating," Energy, Elsevier, vol. 137(C), pages 292-301.
    5. 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).
    6. Eduardo Roque & Romeu Vicente & Ricardo M. S. F. Almeida & Victor M. Ferreira, 2022. "The Impact of Thermal Inertia on the Indoor Thermal Environment of Light Steel Framing Constructions," Energies, MDPI, vol. 15(9), pages 1-17, April.
    7. Liu, Lanbin & Fu, Lin & Jiang, Yi & Guo, Shan, 2011. "Major issues and solutions in the heat-metering reform in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 673-680, January.
    8. Østergaard, Dorte Skaarup & Tunzi, Michele & Svendsen, Svend, 2021. "What does a well-functioning heating system look like? Investigation of ten Danish buildings that utilize district heating efficiently," Energy, Elsevier, vol. 227(C).
    9. 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).
    10. 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).
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    Cited by:

    1. 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).
    2. 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).
    3. 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).

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