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Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings

Author

Listed:
  • Alperen Yayla

    (Department of Civil and Environmental Engineering, Imperial College London, Skempton Building, London SW7 2AZ, UK)

  • Kübra Sultan Świerczewska

    (Cundall Polska, 00-582 Warszawa, Poland)

  • Mahmut Kaya

    (KPD Engineering & Consultancy, Bursa 16090, Türkiye)

  • Bahadır Karaca

    (Nuclear Islands Department, Akkuyu Nuclear Power Plant, Mersin 33715, Türkiye)

  • Yusuf Arayici

    (Department of Architecture and Built Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK)

  • Yunus Emre Ayözen

    (Strategy Development Department, Ministry of Transport and Infrastructure, Ankara 06338, Türkiye)

  • Onur Behzat Tokdemir

    (Department of Civil Engineering, Istanbul Technical University, Istanbul 34467, Türkiye)

Abstract

Buildings are responsible for almost half of the world’s energy consumption, and approximately 40% of total building energy is consumed by the heating ventilation and air conditioning (HVAC) system. The inability of traditional HVAC controllers to respond to sudden changes in occupancy and environmental conditions makes them energy inefficient. Despite the oversimplified building thermal response models and inexact occupancy sensors of traditional building automation systems, investigations into a more efficient and effective sensor-free control mechanism have remained entirely inadequate. This study aims to develop an artificial intelligence (AI)-based occupant-centric HVAC control mechanism for cooling that continually improves its knowledge to increase energy efficiency in a multi-zone commercial building. The study is carried out using two-year occupancy and environmental conditions data of a shopping mall in Istanbul, Turkey. The research model consists of three steps: prediction of hourly occupancy, development of a new HVAC control mechanism, and comparison of the traditional and AI-based control systems via simulation. After determining the attributions for occupancy in the mall, hourly occupancy prediction is made using real data and an artificial neural network (ANN). A sensor-free HVAC control algorithm is developed with the help of occupancy data obtained from the previous stage, building characteristics, and real-time weather forecast information. Finally, a comparison of traditional and AI-based HVAC control mechanisms is performed using IDA Indoor Climate and Energy (ICE) simulation software. The results show that applying AI for HVAC operation achieves savings of a minimum of 10% energy consumption while providing a better thermal comfort level to occupants. The findings of this study demonstrate that the proposed approach can be a very advantageous tool for sustainable development and also used as a standalone control mechanism as it improves.

Suggested Citation

  • Alperen Yayla & Kübra Sultan Świerczewska & Mahmut Kaya & Bahadır Karaca & Yusuf Arayici & Yunus Emre Ayözen & Onur Behzat Tokdemir, 2022. "Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings," Sustainability, MDPI, vol. 14(23), pages 1-29, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16107-:d:991261
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    References listed on IDEAS

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    Cited by:

    1. Aleksander Skała & Jakub Grela & Dominik Latoń & Katarzyna Bańczyk & Michał Markiewicz & Andrzej Ożadowicz, 2023. "Implementation of Building a Thermal Model to Improve Energy Efficiency of the Central Heating System—A Case Study," Energies, MDPI, vol. 16(19), pages 1-27, September.
    2. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.

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