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A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings

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  • Joe, Jaewan
  • Karava, Panagiota

Abstract

This paper introduces a smart operation strategy based on model predictive control (MPC) to optimize the performance of hydronic radiant floor systems in office buildings and presents results from its implementation in an actual building. Our MPC approach uses dynamic estimates and predictions of zone loads and temperatures, outdoor weather conditions, and HVAC system models to minimize energy consumption and cost while meeting equipment and thermal comfort constraints. It includes data-driven building models estimated and validated using data from an actual building, and deploys an optimizer based on constraint linear/quadratic programming with hard comfort bounds that yields a global minimum with predicted exogenous disturbances. The MPC results show 34% cost savings compared to baseline feedback control during the cooling season and 16% energy use reduction during the heating season. Also, the radiant floor system with the predictive controller shows 29–50% energy savings when compared with a baseline air delivery system serving two identical thermal zones located in the same building.

Suggested Citation

  • Joe, Jaewan & Karava, Panagiota, 2019. "A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings," Applied Energy, Elsevier, vol. 245(C), pages 65-77.
    • Handle: RePEc:eee:appene:v:245:y:2019:i:c:p:65-77
    DOI: 10.1016/j.apenergy.2019.03.209
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    References listed on IDEAS

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    1. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    2. Ahn, Byung-Cheon & Song, Jae-Yeob, 2010. "Control characteristics and heating performance analysis of automatic thermostatic valves for radiant slab heating system in residential apartments," Energy, Elsevier, vol. 35(4), pages 1615-1624.
    3. Lehmann, B. & Dorer, V. & Gwerder, M. & Renggli, F. & Tödtli, J., 2011. "Thermally activated building systems (TABS): Energy efficiency as a function of control strategy, hydronic circuit topology and (cold) generation system," Applied Energy, Elsevier, vol. 88(1), pages 180-191, January.
    4. Mirakhorli, Amin & Dong, Bing, 2018. "Model predictive control for building loads connected with a residential distribution grid," Applied Energy, Elsevier, vol. 230(C), pages 627-642.
    5. Gholamibozanjani, Gohar & Tarragona, Joan & Gracia, Alvaro de & Fernández, Cèsar & Cabeza, Luisa F. & Farid, Mohammed M., 2018. "Model predictive control strategy applied to different types of building for space heating," Applied Energy, Elsevier, vol. 231(C), pages 959-971.
    6. Turner, W.J.N. & Walker, I.S. & Roux, J., 2015. "Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass," Energy, Elsevier, vol. 82(C), pages 1057-1067.
    7. Cho, S.-H & Zaheer-uddin, M, 1999. "An experimental study of multiple parameter switching control for radiant floor heating systems," Energy, Elsevier, vol. 24(5), pages 433-444.
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