IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2020i1p138-d469905.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/1/138/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/1/138/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús & García-Lozano, César, 2016. "Update of energy performance certificates in the residential sector and scenarios that consider the impact of automation, control and management systems: A case study of La Rioja," Applied Energy, Elsevier, vol. 178(C), pages 308-322.
    2. Maria-Mar Fernandez-Antolin & José-Manuel del-Río & Roberto-Alonso Gonzalez-Lezcano, 2019. "Influence of Solar Reflectance and Renewable Energies on Residential Heating and Cooling Demand in Sustainable Architecture: A Case Study in Different Climate Zones in Spain Considering Their Urban Co," Sustainability, MDPI, vol. 11(23), pages 1-31, November.
    3. Schweiker, Marcel & Shukuya, Masanori, 2010. "Comparative effects of building envelope improvements and occupant behavioural changes on the exergy consumption for heating and cooling," Energy Policy, Elsevier, vol. 38(6), pages 2976-2986, June.
    4. Marinakis, Vangelis & Doukas, Haris & Karakosta, Charikleia & Psarras, John, 2013. "An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector," Applied Energy, Elsevier, vol. 101(C), pages 6-14.
    5. Kim, Jimin & Hong, Taehoon & Jeong, Jaemin & Lee, Myeonghwi & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan & Jeong, Jaewook, 2017. "Establishment of an optimal occupant behavior considering the energy consumption and indoor environmental quality by region," Applied Energy, Elsevier, vol. 204(C), pages 1431-1443.
    6. Nam-Kyu Kim & Myung-Hyun Shim & Dongjun Won, 2018. "Building Energy Management Strategy Using an HVAC System and Energy Storage System," Energies, MDPI, vol. 11(10), pages 1-15, October.
    7. Gaetani, Isabella & Hoes, Pieter-Jan & Hensen, Jan L.M., 2018. "Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run," Applied Energy, Elsevier, vol. 223(C), pages 159-171.
    8. Langevin, Jared & Gurian, Patrick L. & Wen, Jin, 2013. "Reducing energy consumption in low income public housing: Interviewing residents about energy behaviors," Applied Energy, Elsevier, vol. 102(C), pages 1358-1370.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hasim Altan & Bertug Ozarisoy, 2022. "An Analysis of the Development of Modular Building Design Elements to Improve Thermal Performance of a Representative High Rise Residential Estate in the Coastline City of Famagusta, Cyprus," Sustainability, MDPI, vol. 14(7), pages 1-50, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Antonio Paone & Jean-Philippe Bacher, 2018. "The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art," Energies, MDPI, vol. 11(4), pages 1-19, April.
    2. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. Xu, Xiaojing & Chen, Chien-fei, 2019. "Energy efficiency and energy justice for U.S. low-income households: An analysis of multifaceted challenges and potential," Energy Policy, Elsevier, vol. 128(C), pages 763-774.
    4. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    5. Zizzo, G. & Beccali, M. & Bonomolo, M. & Di Pietra, B. & Ippolito, M.G. & La Cascia, D. & Leone, G. & Lo Brano, V. & Monteleone, F., 2017. "A feasibility study of some DSM enabling solutions in small islands: The case of Lampedusa," Energy, Elsevier, vol. 140(P1), pages 1030-1046.
    6. Liu, Wenling & Zhang, Jinyun & Bluemling, Bettina & Mol, Arthur P.J. & Wang, Can, 2015. "Public participation in energy saving retrofitting of residential buildings in China," Applied Energy, Elsevier, vol. 147(C), pages 287-296.
    7. Zhikun Ding & Rongsheng Liu & Zongjie Li & Cheng Fan, 2020. "A Thematic Network-Based Methodology for the Research Trend Identification in Building Energy Management," Energies, MDPI, vol. 13(18), pages 1-33, September.
    8. Habtamu Tkubet Ebuy & Hind Bril El Haouzi & Riad Benelmir & Remi Pannequin, 2023. "Occupant Behavior Impact on Building Sustainability Performance: A Literature Review," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    9. Andrea Okanović & Jelena Ješić & Vladimir Đaković & Simonida Vukadinović & Andrea Andrejević Panić, 2021. "Increasing University Competitiveness through Assessment of Green Content in Curriculum and Eco-Labeling in Higher Education," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    10. Sylwia Słupik & Joanna Kos-Łabędowicz & Joanna Trzęsiok, 2021. "Energy-Related Behaviour of Consumers from the Silesia Province (Poland)—Towards a Low-Carbon Economy," Energies, MDPI, vol. 14(8), pages 1-23, April.
    11. Pothitou, Mary & Hanna, Richard F. & Chalvatzis, Konstantinos J., 2016. "Environmental knowledge, pro-environmental behaviour and energy savings in households: An empirical study," Applied Energy, Elsevier, vol. 184(C), pages 1217-1229.
    12. Koasidis, Konstantinos & Marinakis, Vangelis & Nikas, Alexandros & Chira, Katerina & Flamos, Alexandros & Doukas, Haris, 2022. "Monetising behavioural change as a policy measure to support energy management in the residential sector: A case study in Greece," Energy Policy, Elsevier, vol. 161(C).
    13. Sarran, Lucile & Gunay, H. Burak & O'Brien, William & Hviid, Christian A. & Rode, Carsten, 2021. "A data-driven study of thermostat overrides during demand response events," Energy Policy, Elsevier, vol. 153(C).
    14. Chen, Chien-fei & Xu, Xiaojing & Adua, Lazarus & Briggs, Morgan & Nelson, Hannah, 2022. "Exploring the factors that influence energy use intensity across low-, middle-, and high-income households in the United States," Energy Policy, Elsevier, vol. 168(C).
    15. Kools, L. & Phillipson, F., 2016. "Data granularity and the optimal planning of distributed generation," Energy, Elsevier, vol. 112(C), pages 342-352.
    16. Aldubyan, Mohammad & Krarti, Moncef, 2022. "Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study," Energy, Elsevier, vol. 238(PA).
    17. Ascione, Fabrizio & Bianco, Nicola & de’ Rossi, Filippo & Turni, Gianluca & Vanoli, Giuseppe Peter, 2013. "Green roofs in European climates. Are effective solutions for the energy savings in air-conditioning?," Applied Energy, Elsevier, vol. 104(C), pages 845-859.
    18. Kola-Bezka, Maria & Leki, Krzysztof, 2024. "Household energy behaviour in the times of crisis: Lessons for policy initiatives from peripheral, fossil-dependent regions of the European Union," Energy Policy, Elsevier, vol. 188(C).
    19. Wang, S. & Kim, A.A. & Johnson, E.M., 2017. "Understanding the deterministic and probabilistic business cases for occupant based plug load management strategies in commercial office buildings," Applied Energy, Elsevier, vol. 191(C), pages 398-413.
    20. Hye-Ryeong Nam & Seo-Hoon Kim & Seol-Yee Han & Sung-Jin Lee & Won-Hwa Hong & Jong-Hun Kim, 2020. "Statistical Methodology for the Definition of Standard Model for Energy Analysis of Residential Buildings in Korea," Energies, MDPI, vol. 13(21), pages 1-16, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:138-:d:469905. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.