Citation: Yayla, A.;
´
Swierczewska,
K.S.; Kaya, M.; Karaca, B.; Arayici, Y.;
Ayözen, Y.E.; Tokdemir, O.B.
Artificial Intelligence (AI)-Based
Occupant-Centric Heating
Ventilation and Air Conditioning
(HVAC) Control System for
Multi-Zone Commercial Buildings.
Sustainability 2022, 14, 16107.
https://doi.org/10.3390/su142316107
Academic Editors: Luis
Hernández-Callejo, Sergio
Nesmachnow and Sara
Gallardo Saavedra
Received: 11 October 2022
Accepted: 12 November 2022
Published: 2 December 2022
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Article
Artificial Intelligence (AI)-Based Occupant-Centric Heating
Ventilation and Air Conditioning (HVAC) Control System for
Multi-Zone Commercial Buildings
Alperen Yayla
1
, Kübra Sultan
´
Swierczewska
2
, Mahmut Kaya
3
, Bahadır Karaca
4
, Yusuf Arayici
5
,
Yunus Emre Ayözen
6
and Onur Behzat Tokdemir
7,
*
1
Department of Civil and Environmental Engineering, Imperial College London, Skempton Building,
London SW7 2AZ, UK
2
Cundall Polska, 00-582 Warszawa, Poland
3
KPD Engineering & Consultancy, Bursa 16090, Türkiye
4
Nuclear Islands Department, Akkuyu Nuclear Power Plant, Mersin 33715, Türkiye
5
Department of Architecture and Built Environment, Northumbria University,
Newcastle upon Tyne NE1 8ST, UK
6
Strategy Development Department, Ministry of Transport and Infrastructure, Ankara 06338, Türkiye
7
Department of Civil Engineering, Istanbul Technical University, Istanbul 34467, Türkiye
* Correspondence: otokdemir@itu.edu.tr
Abstract:
Buildings are responsible for almost half of the world’s energy consumption, and approx-
imately 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 oversim-
plified 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
1. Introduction
Due to high demand and the need for an increasing energy supply, energy efficiency
becomes crucial. Restricted energy markets have wide effects in areas ranging from house-
hold budgets to international relations. Thus, due to high energy consumption, buildings
are on the front line of energy efficiency research. Buildings compose approximately 40%
Sustainability 2022, 14, 16107. https://doi.org/10.3390/su142316107 https://www.mdpi.com/journal/sustainability
