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Building Energy Management for Passive Cooling Based on Stochastic Occupants Behavior Evaluation

Author

Listed:
  • Michele Roccotelli

    (Department of Electric and Information Engineering, Faculty of Engineering, Polytechnic University of Bari, 70126 Bari, Italy
    These authors contributed equally to this work.)

  • Alessandro Rinaldi

    (Department of Electric and Information Engineering, Faculty of Engineering, Polytechnic University of Bari, 70126 Bari, Italy
    These authors contributed equally to this work.)

  • Maria Pia Fanti

    (Department of Electric and Information Engineering, Faculty of Engineering, Polytechnic University of Bari, 70126 Bari, Italy
    These authors contributed equally to this work.)

  • Francesco Iannone

    (Department of Civil, Environmental, Land, Building Engineering and Chemistry, Faculty of Engineering, Polytechnic University of Bari, 70126 Bari, Italy
    These authors contributed equally to this work.)

Abstract

The common approach to model occupants behaviors in buildings is deterministic and consists of assumptions based on predefined fixed schedules or rules. In contrast with the deterministic models, stochastic and agent based (AB) models are the most powerful and suitable methods for modeling complex systems as the human behavior. In this paper, a co-simulation architecture is proposed with the aim of modeling the occupant behavior in buildings by a stochastic-AB approach and implementing an intelligent Building Energy Management System (BEMS). In particular, optimized control logics are designed for smart passive cooling by controlling natural ventilation and solar shading systems to guarantee the thermal comfort conditions and maintain energy performance. Moreover, the effects of occupant actions on indoor thermal comfort are also taken into account. This study shows how the integration of automation systems and passive techniques increases the potentialities of passive cooling in buildings, integrating or replacing the conventional efficiency strategies.

Suggested Citation

  • Michele Roccotelli & Alessandro Rinaldi & Maria Pia Fanti & Francesco Iannone, 2020. "Building Energy Management for Passive Cooling Based on Stochastic Occupants Behavior Evaluation," Energies, MDPI, vol. 14(1), pages 1-24, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:138-:d:469905
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    References listed on IDEAS

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    Cited by:

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    2. Nuno Baía Saraiva & Luisa Dias Pereira & Adélio Rodrigues Gaspar & José Joaquim da Costa, 2021. "Barriers on Establishing Passive Strategies in Office Spaces: A Case Study in a Historic University Building," Sustainability, MDPI, vol. 13(8), pages 1-15, April.
    3. Jihoon Jang & Jinmog Han & Min-Hwi Kim & Deuk-won Kim & Seung-Bok Leigh, 2021. "Extracting Influential Factors for Building Energy Consumption via Data Mining Approaches," Energies, MDPI, vol. 14(24), pages 1-19, December.

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