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Potential of predictive control for improvement of seasonal coefficient of performance of air source heat pump in Central European climate zone

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  • Pospíšil, Jiří
  • Špiláček, Michal
  • Kudela, Libor

Abstract

This paper compares different operation models of the air-to-water heat pump (HP). Detail focus of this study aims at a potential to increase seasonal coefficient of performance (SCOP) by utilising the predictive control. The considered predictive control uses an outdoor air temperature forecast for the upcoming 48 h. The predictive control operates the heat pump so that it runs, preferably, during the periods of the day with the highest air temperature. For a detailed assessment, a model of the heat supply system with a heat pump supplemented by a heat accumulator has been developed. The mathematical model involves detailed algorithm for time-dependent quantification of the heat demand for the considered model building. Dataset of real operation tests of the HP helps correctly evaluate the coefficient of performance (COP). An original algorithm of predictive control has been developed and tested for different operating parameters and different capacities of the heat accumulator. A long-term record of air temperatures from the last ten years is employed to evaluate the model. The mathematical model allows for a complex parametrical study to evaluate the relations of SCOP - accumulator capacity, SCOP - method of heat pump control.

Suggested Citation

  • Pospíšil, Jiří & Špiláček, Michal & Kudela, Libor, 2018. "Potential of predictive control for improvement of seasonal coefficient of performance of air source heat pump in Central European climate zone," Energy, Elsevier, vol. 154(C), pages 415-423.
    • Handle: RePEc:eee:energy:v:154:y:2018:i:c:p:415-423
    DOI: 10.1016/j.energy.2018.04.131
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    Cited by:

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    3. Jiří Pospíšil & Michal Špiláček & Pavel Charvát, 2019. "Seasonal COP of an Air-to-Water Heat Pump when Using Predictive Control Preferring Power Production from Renewable Sources in the Czech Republic," Energies, MDPI, vol. 12(17), pages 1-13, August.
    4. Rödder, Maximilian & Frank, Lena & Kirschner, Daniel & Neef, Matthias & Adam, Mario, 2018. "EnergiBUS4home – Sustainable energy resourcing in low-energy buildings," Energy, Elsevier, vol. 159(C), pages 638-647.
    5. O'Hegarty, R. & Kinnane, O. & Lennon, D. & Colclough, S., 2022. "Air-to-water heat pumps: Review and analysis of the performance gap between in-use and product rated performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    6. Kudela, Libor & Špiláček, Michal & Pospíšil, Jiří, 2021. "Influence of control strategy on seasonal coefficient of performance for a heat pump with low-temperature heat storage in the geographical conditions of Central Europe," Energy, Elsevier, vol. 234(C).
    7. Valeria Palomba & Efstratios Varvagiannis & Sotirios Karellas & Andrea Frazzica, 2019. "Hybrid Adsorption-Compression Systems for Air Conditioning in Efficient Buildings: Design through Validated Dynamic Models," Energies, MDPI, vol. 12(6), pages 1-28, March.
    8. Han, Gwangwoo & Joo, Hong-Jin & Lim, Hee-Won & An, Young-Sub & Lee, Wang-Je & Lee, Kyoung-Ho, 2023. "Data-driven heat pump operation strategy using rainbow deep reinforcement learning for significant reduction of electricity cost," Energy, Elsevier, vol. 270(C).
    9. Liu, Hongxun & Mauzerall, Denise L., 2020. "Costs of clean heating in China: Evidence from rural households in the Beijing-Tianjin-Hebei region," Energy Economics, Elsevier, vol. 90(C).
    10. Oravec, Juraj & Bakošová, Monika & Galčíková, Lenka & Slávik, Michal & Horváthová, Michaela & Mészáros, Alajos, 2019. "Soft-constrained robust model predictive control of a plate heat exchanger: Experimental analysis," Energy, Elsevier, vol. 180(C), pages 303-314.
    11. Sun, Xiaoyu & Wang, Zhichao & Li, Xiaofeng & Xu, Zhaowei & Yang, Qiang & Yang, Yingxia, 2021. "Seasonal heating performance prediction of air-to-water heat pumps based on short-term dynamic monitoring," Renewable Energy, Elsevier, vol. 180(C), pages 829-837.
    12. Vilén, Karl & Ahlgren, Erik O., 2023. "Linear or mixed integer programming in long-term energy systems modeling – A comparative analysis for a local expanding heating system," Energy, Elsevier, vol. 283(C).
    13. Martin, Nick & Zinck Thellufsen, Jakob & Chang, Miguel & Talens-Peiró, Laura & Madrid-López, Cristina, 2024. "The many faces of heating transitions. Deeper understandings of future systems in Sweden and beyond," Energy, Elsevier, vol. 290(C).

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