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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. Oldewurtel, Frauke & Sturzenegger, David & Morari, Manfred, 2013. "Importance of occupancy information for building climate control," Applied Energy, Elsevier, vol. 101(C), pages 521-532.
    3. 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.
    4. 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.
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    14. 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.
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