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Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings

Author

Listed:
  • Panagiotis Korkidis

    (Department of Biomedical Engineering, Egaleo Park Campus, University of West Attica, 12243 Athina, Greece
    These authors contributed equally to this work.)

  • Anastasios Dounis

    (Department of Biomedical Engineering, Egaleo Park Campus, University of West Attica, 12243 Athina, Greece
    These authors contributed equally to this work.)

  • Panagiotis Kofinas

    (Department of Biomedical Engineering, Egaleo Park Campus, University of West Attica, 12243 Athina, Greece
    These authors contributed equally to this work.)

Abstract

This paper focuses on the development of a multi agent control system (MACS), combined with a stochastic based approach for occupancy estimation. The control framework aims to maintain the comfort levels of a building in high levels and reduce the overall energy consumption. Three independent agents, each dedicated to the thermal comfort, the visual comfort, and the indoor air quality, are deployed. A stochastic model describing the CO 2 concentration has been studied, focused on the occupancy estimation problem. A probabilistic approach, as well as an evolutionary algorithm, are used to provide insights on the stochastic model. Moreover, in order to induce uncertainty, parameters are treated in a fuzzy modelling framework and the results on the occupancy estimation are investigated. In the control framework, to cope with the continuous state-action space, the three agents utilise Fuzzy Q -learning. Simulation results highlight the precision of parameter and occupancy estimation, as well as the high capabilities of the control framework, when taking into account the occupancy state, as energy consumption is reduced by 55.9 % , while the overall comfort index is kept in high levels, with values close to one.

Suggested Citation

  • Panagiotis Korkidis & Anastasios Dounis & Panagiotis Kofinas, 2021. "Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings," Energies, MDPI, vol. 14(16), pages 1-33, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4971-:d:613924
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    References listed on IDEAS

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    Cited by:

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    2. V. S. K. V. Harish & Arun Kumar & Tabish Alam & Paolo Blecich, 2021. "Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability," Sustainability, MDPI, vol. 13(21), pages 1-26, October.
    3. Dimitrios K. Panagiotou & Anastasios I. Dounis, 2022. "Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network," Energies, MDPI, vol. 15(17), pages 1-25, September.
    4. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.

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