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Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach

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  • Baldi, Simone
  • Korkas, Christos D.
  • Lv, Maolong
  • Kosmatopoulos, Elias B.

Abstract

Load management actions in large buildings are pre-programmed by field engineers/users in the form of if-then-else rules for the set point of the thermostat. This fixed set of actions prevents smart zoning, i.e. to dynamically regulate the set points in every room at different levels according to geometry, orientation and interaction among rooms caused by occupancy patterns. In this work we frame the problem of load management with smart zoning into a multiple-mode feedback-based optimal control problem: multiple-mode refers to embedding multiple behaviors (triggered by building-occupant dynamic interaction) into the optimization problem; feedback-based refers to adopting a Hamilton-Jacobi-Bellman framework, with closed-loop control strategies using information stemming from building and weather states. The framework is solved by parameterizing the candidate control strategies and by searching for the optimal strategy in an adaptive self-tuning way. To demonstrate the proposed approach, we employ an EnergyPlus model of an actual office building in Crete, Greece. Extensive tests show that the proposed solution is able to provide, dynamically and autonomously, dedicated set points levels in every room in such a way to optimize the whole building performance (exploitation of renewable energy sources with improved thermal comfort). As compared to pre-programmed (non-optimal) strategies, we show that smart zoning makes it is possible to save more than 15% energy consumption, with 25% increased thermal comfort. As compared to optimized strategies in which smart zoning is not implemented, smart zoning leads to additional 4% reduced energy and 8% improved comfort, demonstrating improved occupant-building interaction. Such improvements are motivated by the fact that the approach exploits the building dynamics as learned from feedback data. Moreover, the closed-loop feature of the approach makes it robust to variable weather conditions and occupancy schedules.

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  • Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    • Handle: RePEc:eee:appene:v:231:y:2018:i:c:p:1246-1258
    DOI: 10.1016/j.apenergy.2018.09.188
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    Cited by:

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    4. Li, Jinwei & Ma, Rongjiang & Deng, Mengsi & Cao, Xiaoling & Wang, Xicheng & Wang, Xianlin, 2024. "A comparative study of clustering algorithms for intermittent heating demand considering time series," Applied Energy, Elsevier, vol. 353(PA).
    5. Hyeunguk Ahn & Jingjing Liu & Donghun Kim & Rongxin Yin & Tianzhen Hong & Mary Ann Piette, 2021. "How Can Floor Covering Influence Buildings’ Demand Flexibility?," Energies, MDPI, vol. 14(12), pages 1-17, June.
    6. Li, Yang & Bu, Fanjin & Li, Yuanzheng & Long, Chao, 2023. "Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 333(C).
    7. Siva Swaminathan & Ximan Wang & Bingyu Zhou & Simone Baldi, 2018. "A University Building Test Case for Occupancy-Based Building Automation," Energies, MDPI, vol. 11(11), pages 1-15, November.
    8. Yan Ding & Xiao Pan & Wanyue Chen & Zhe Tian & Zhiyao Wang & Qing He, 2022. "Prediction Method for Office Building Energy Consumption Based on an Agent-Based Model Considering Occupant–Equipment Interaction Behavior," Energies, MDPI, vol. 15(22), pages 1-31, November.
    9. Favero, Matteo & Kloppenborg Møller, Jan & Calì, Davide & Carlucci, Salvatore, 2022. "Human-in-the-loop methods for occupant-centric building design and operation," Applied Energy, Elsevier, vol. 325(C).

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