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Model-based predictive maintenance in building automation systems with user discomfort

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  • Cauchi, Nathalie
  • Macek, Karel
  • Abate, Alessandro

Abstract

This work presents a new methodology for quantifying the discomfort caused by non-optimal temperature regulation, in a building automation system, as a result of degraded biomass boiler operation. This discomfort is incorporated in a model-based dynamic programming algorithm that computes the optimal maintenance action for cleaning or replacing the boiler. A non-linear cleaning model is used to represent the different cleaning strategies under taken by contractors. The maintenance strategy minimizes the total operational costs of the boiler, the cleaning costs and the newly defined discomfort costs, over a long-term prediction horizon that captures the short-term daily thermal comfort within the heating zone. The approach has been developed based on real data obtained from a biomass boiler at a Spanish school and the resulting optimal maintenance strategies are shown to have the potential of significant energy and cost savings.

Suggested Citation

  • Cauchi, Nathalie & Macek, Karel & Abate, Alessandro, 2017. "Model-based predictive maintenance in building automation systems with user discomfort," Energy, Elsevier, vol. 138(C), pages 306-315.
    • Handle: RePEc:eee:energy:v:138:y:2017:i:c:p:306-315
    DOI: 10.1016/j.energy.2017.07.104
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    References listed on IDEAS

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    Cited by:

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    2. Georgios I. Tsoumalis & Zafeirios N. Bampos & Georgios V. Chatzis & Pandelis N. Biskas, 2022. "Overview of Natural Gas Boiler Optimization Technologies and Potential Applications on Gas Load Balancing Services," Energies, MDPI, vol. 15(22), pages 1-24, November.
    3. Toubeau, Jean-François & Pardoen, Lorie & Hubert, Louis & Marenne, Nicolas & Sprooten, Jonathan & De Grève, Zacharie & Vallée, François, 2022. "Machine learning-assisted outage planning for maintenance activities in power systems with renewables," Energy, Elsevier, vol. 238(PC).
    4. Sławomir Kłos & Justyna Patalas-Maliszewska & Łukasz Piechowicz & Krzysztof Wachowski, 2021. "Analysis and Predicting the Energy Consumption of Low-Pressure Carburising Processes," Energies, MDPI, vol. 14(12), pages 1-12, June.
    5. Bonomolo, Marina & Zizzo, Gaetano & Ferrari, Simone & Beccali, Marco & Guarino, Stefania, 2021. "Empirical BAC factors method application to two real case studies in South Italy," Energy, Elsevier, vol. 236(C).
    6. Zhu, Xu & Zhang, Shuai & Jin, Xinqiao & Du, Zhimin, 2020. "Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency," Energy, Elsevier, vol. 213(C).
    7. Baldi, Simone & Zhang, Fan & Le Quang, Thuan & Endel, Petr & Holub, Ondrej, 2019. "Passive versus active learning in operation and adaptive maintenance of Heating, Ventilation, and Air Conditioning," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    8. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).

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