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

Alperen Yayla, Kübra Sultan Świerczewska, Mahmut Kaya, Bahadır Karaca, Yusuf Arayici, Yunus Emre Ayözen and Onur Behzat Tokdemir
Additional contact information
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

Sustainability, 2022, vol. 14, issue 23, 1-29

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.

Keywords: artificial intelligence (AI); automatic HVAC control; occupant behavior; model predictive control; energy efficiency (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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