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Towards the real-life implementation of MPC for an office building: Identification issues

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  • Žáčeková, Eva
  • Váňa, Zdeněk
  • Cigler, Jiří

Abstract

Modern control methods such as Model Predictive Control (MPC) are getting popular in recent years in many fields of industry. One of the branches that have witnessed great increase of interest in use of the MPC over the last few years is the building climate control area. According to the studies, the energy used in the building sector counts for 20–40% of the overall energy consumption. Almost half of this amount consists of heating, ventilation and air-conditioning (HVAC) costs which implies that energy consumption decrease in this area is one of the most interesting challenges today.

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  • Žáčeková, Eva & Váňa, Zdeněk & Cigler, Jiří, 2014. "Towards the real-life implementation of MPC for an office building: Identification issues," Applied Energy, Elsevier, vol. 135(C), pages 53-62.
    • Handle: RePEc:eee:appene:v:135:y:2014:i:c:p:53-62
    DOI: 10.1016/j.apenergy.2014.08.004
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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. Petersen, Steffen & Bundgaard, Katrine Wieck, 2014. "The effect of weather forecast uncertainty on a predictive control concept for building systems operation," Applied Energy, Elsevier, vol. 116(C), pages 311-321.
    3. Oldewurtel, Frauke & Sturzenegger, David & Morari, Manfred, 2013. "Importance of occupancy information for building climate control," Applied Energy, Elsevier, vol. 101(C), pages 521-532.
    4. Wang, Nan & Zhang, Jiangfeng & Xia, Xiaohua, 2013. "Desiccant wheel thermal performance modeling for indoor humidity optimal control," Applied Energy, Elsevier, vol. 112(C), pages 999-1005.
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    Cited by:

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    5. Chen, Xiao & Wang, Qian & Srebric, Jelena, 2016. "Occupant feedback based model predictive control for thermal comfort and energy optimization: A chamber experimental evaluation," Applied Energy, Elsevier, vol. 164(C), pages 341-351.
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    12. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    13. Bünning, Felix & Huber, Benjamin & Schalbetter, Adrian & Aboudonia, Ahmed & Hudoba de Badyn, Mathias & Heer, Philipp & Smith, Roy S. & Lygeros, John, 2022. "Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC," Applied Energy, Elsevier, vol. 310(C).
    14. Khodabakhshian, Mohammad & Feng, Lei & Börjesson, Stefan & Lindgärde, Olof & Wikander, Jan, 2017. "Reducing auxiliary energy consumption of heavy trucks by onboard prediction and real-time optimization," Applied Energy, Elsevier, vol. 188(C), pages 652-671.
    15. Blum, D.H. & Arendt, K. & Rivalin, L. & Piette, M.A. & Wetter, M. & Veje, C.T., 2019. "Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems," Applied Energy, Elsevier, vol. 236(C), pages 410-425.
    16. Razmara, M. & Maasoumy, M. & Shahbakhti, M. & Robinett, R.D., 2015. "Optimal exergy control of building HVAC system," Applied Energy, Elsevier, vol. 156(C), pages 555-565.
    17. Gruber, Mattias & Trüschel, Anders & Dalenbäck, Jan-Olof, 2015. "Energy efficient climate control in office buildings without giving up implementability," Applied Energy, Elsevier, vol. 154(C), pages 934-943.
    18. Ferracuti, Francesco & Fonti, Alessandro & Ciabattoni, Lucio & Pizzuti, Stefano & Arteconi, Alessia & Helsen, Lieve & Comodi, Gabriele, 2017. "Data-driven models for short-term thermal behaviour prediction in real buildings," Applied Energy, Elsevier, vol. 204(C), pages 1375-1387.
    19. Michailidis, Iakovos T. & Schild, Thomas & Sangi, Roozbeh & Michailidis, Panagiotis & Korkas, Christos & Fütterer, Johannes & Müller, Dirk & Kosmatopoulos, Elias B., 2018. "Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study," Applied Energy, Elsevier, vol. 211(C), pages 113-125.
    20. Samy Faddel & Guanyu Tian & Qun Zhou, 2021. "Decentralized Management of Commercial HVAC Systems," Energies, MDPI, vol. 14(11), pages 1-18, May.
    21. Malin Lachmann & Jaime Maldonado & Wiebke Bergmann & Francesca Jung & Markus Weber & Christof Büskens, 2020. "Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System," Energies, MDPI, vol. 13(8), pages 1-42, April.

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