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Experimental test of a black-box economic model predictive control for residential space heating

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  • Knudsen, Michael Dahl
  • Georges, Laurent
  • Skeie, Kristian Stenerud
  • Petersen, Steffen

Abstract

Previous studies have identified significant demand response (DR) potentials in using economic model predictive control (E-MPC) of space heating to exploit the inherent thermal mass in residential buildings for short-term energy storage. However, the economically viable realisation of E-MPC in residential buildings requires an effort to minimise the need for additional equipment and labour-intensive modelling processes. This paper reports on an experiment where a novel E-MPC setup was used for thermostatically control of a hydronic radiator in a highly-insulated residential building located on the NTNU Campus in Trondheim, Norway. The E-MPC utilized data from a heating meter, two temperature sensors and an existing weather forecast web service to train a linear black-box model. The results showed that the precision of model trained on excitation data that was generated using setpoints of either 21 or 24 °C was sufficient to obtain good control of the indoor air temperature while shifting consumption from high to low price periods. The findings of the experiment indicate that a minimal E-MPC setup is able to realize the significant DR potential that lies in utilizing the inherent thermal mass in residential buildings.

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  • Knudsen, Michael Dahl & Georges, Laurent & Skeie, Kristian Stenerud & Petersen, Steffen, 2021. "Experimental test of a black-box economic model predictive control for residential space heating," Applied Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921006498
    DOI: 10.1016/j.apenergy.2021.117227
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    References listed on IDEAS

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    1. 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.
    2. Le Dréau, J. & Heiselberg, P., 2016. "Energy flexibility of residential buildings using short term heat storage in the thermal mass," Energy, Elsevier, vol. 111(C), pages 991-1002.
    3. Hedegaard, Rasmus Elbæk & Kristensen, Martin Heine & Pedersen, Theis Heidmann & Brun, Adam & Petersen, Steffen, 2019. "Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response," Applied Energy, Elsevier, vol. 242(C), pages 181-204.
    4. Kuboth, Sebastian & Heberle, Florian & König-Haagen, Andreas & Brüggemann, Dieter, 2019. "Economic model predictive control of combined thermal and electric residential building energy systems," Applied Energy, Elsevier, vol. 240(C), pages 372-385.
    5. Finck, Christian & Li, Rongling & Zeiler, Wim, 2019. "Economic model predictive control for demand flexibility of a residential building," Energy, Elsevier, vol. 176(C), pages 365-379.
    6. Esther, B. Priya & Kumar, K. Sathish, 2016. "A survey on residential Demand Side Management architecture, approaches, optimization models and methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 342-351.
    7. O׳Connell, Niamh & Pinson, Pierre & Madsen, Henrik & O׳Malley, Mark, 2014. "Benefits and challenges of electrical demand response: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 686-699.
    8. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. Dominković, D.F. & Gianniou, P. & Münster, M. & Heller, A. & Rode, C., 2018. "Utilizing thermal building mass for storage in district heating systems: Combined building level simulations and system level optimization," Energy, Elsevier, vol. 153(C), pages 949-966.
    10. Reynders, Glenn & Diriken, Jan & Saelens, Dirk, 2017. "Generic characterization method for energy flexibility: Applied to structural thermal storage in residential buildings," Applied Energy, Elsevier, vol. 198(C), pages 192-202.
    11. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
    12. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    13. Cai, Hanmin & Ziras, Charalampos & You, Shi & Li, Rongling & Honoré, Kristian & Bindner, Henrik W., 2018. "Demand side management in urban district heating networks," Applied Energy, Elsevier, vol. 230(C), pages 506-518.
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    Cited by:

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    3. Pergantis, Elias N. & Priyadarshan, & Theeb, Nadah Al & Dhillon, Parveen & Ore, Jonathan P. & Ziviani, Davide & Groll, Eckhard A. & Kircher, Kevin J., 2024. "Field demonstration of predictive heating control for an all-electric house in a cold climate," Applied Energy, Elsevier, vol. 360(C).

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