A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings
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DOI: 10.1016/j.apenergy.2019.03.209
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References listed on IDEAS
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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Keywords
Radiant floor system; Thermally Activated Building Systems (TABS); Model predictive control; Field implementation;All these keywords.
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