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

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.

Potential Role and Challenges of ChatGPT and Similar Generative Artificial Intelligence in Architectural Engineering

“…structures, and industrial facilities. In recent years, the integration of Artificial Intelligence (AI) and machine learning has initiated a transformative wave within the HVAC industry, leading to more intelligent, energyefficient, and cost-effective systems (Zhang et al, 2024;Alden et al, 2021;Yayla et al, 2022). Among these AI technologies, generative artificial intelligence, such as ChatGPT, demonstrates potential in addressing an array of challenges and enhancing HVAC systems (Kwon, 2023;Song et al, 2023).…”

Advanced controls on energy reliability, flexibility and occupant-centric control for smart and energy-efficient buildings

“…predicting occupants' presence/behavior. For instance, Yayla et al [149] proposed a HVAC control mechanism that schedules set-point based on hourly predicted occupancy counts from an ANN model. Through comparing with a traditional control strategy that sets set-point temperature based on real-time measured occupancy-related data, the AI-based OCC achieves over 10% energy saving while providing a more thermal comfort indoor environment in a shopping mall, located in Istanbul, Turkey.…”

Metaheuristic-Based Algorithms for Optimizing Fractional-Order Controllers—A Recent, Systematic, and Comprehensive Review

“…Accordingly, control systems are usually developed to efficiently monitor, assess, and regulate the parameters that can influence the environmental system's sustainability. The applications of control systems in environmental sustainability may include waste [2] and pollution [3]; energy consumption [4]; ventilation [5]; carbon emissions [6]; air conditioning [7]; lighting [8]; renewable energy [9]; etc. In addition, control systems have a significant role in ensuring sustainability in industrial processes such as water [10] and power generators [11].…”

“…* The number of citations listed in this table was last updated on 5 June 2023.Fractal Fract. 2023,7, 553 …”