*Application of Artificial Intelligence in Air Conditioning Systems DOI: http://dx.doi.org/10.5772/intechopen.107379*

#### **Figure 3.**

*Projected electricity statistics and carbon emissions till 2050 [3].*

Additionally, it is anticipated that implementing the carbon tax will significantly reduce carbon emissions starting in 2025 [3]. In order to achieve net-zero carbon buildings, energy efficiency upgrades made to existing structures and energyefficient designs for new buildings, including passive and active technology, will be crucial.

The built environment is seriously threatened by overpopulation and rapid urbanization. By 2050, the world's population is expected to reach 9.6 billion, a 21 percent increase from the current number. Therefore, the energy demand, particularly electricity for the built environment, will rise dramatically unless energysaving options and measures are implemented. Moreover, 59% of the world population, as shown in **Figure 4**, resided in highly urbanized regions in 2020 because

**Figure 4.** *World's Population residing in urban and rural areas [4].* these regions have employment opportunities, living standards, and ease of commute. After 2007, the proportion of urban residents overtook rural residents, sharply increasing the need for cooling and heating systems in residential and commercial structures. Urbanization significantly increased ambient temperature and decreased cooling system effectiveness due to the heat island effect. According to the IEA, two-thirds of homes may have air conditioning units [5]. By 2100, the average worldwide temperature could rise by 4°C due to the sharply increasing trend in the deployment of air conditioning systems in urban areas. Therefore, there is an urgent need to implement smart and energy-efficient air conditioning systems, including both passive and active cooling systems, for existing and new buildings. Doing so will lead to achieving net-zero carbon buildings.

Digitalization is a crucial component of the movement toward intelligent and energy-efficient solutions that are required to reach the targets of net-zero carbon emissions. Digitalization enables numerous energy systems to be more interconnected, intelligent, dependable, sustainable, and efficient. Digitalization could reduce energy consumption in buildings by around 10% by using real-time data to increase operational effectiveness. The installation of smart thermostats can also better predict heating and cooling requirements by employing self-learning algorithms, and real-time weather forecasts to predict occupant behavior.

### **2. Application of artificial intelligence in air conditioning systems**

Machine learning (ML), a subset of *artificial intelligence*, widely applies to various sectors. The development of instrumentation and sensors has led to a significant increase in the amount of data collected per minute. Plotting and analyzing these data is crucial to turn them into insightful information that can be used for planning, operations, and forecasting. Machine learning techniques provide the link between the input parameters and the predicted output variables. Machine learning can be generally categorized into two groups, namely (i) superv*ise*d learning and (ii) unsuperv*ise*d learning.

By deploying the appropriate methods, ML can be applied to the followings:


Globally, many countries are embracing digitization, which will help businesses increase productivity, lower operating costs, and improve safety. Additionally, researchers and industry participants can create machine learning and artificial intelligence algorithms using historical data because it is easier to acquire thanks to digitization. However, even though machine learning is developing quickly, there have been difficulties using it in practical applications because it needs a vast amount of data. However, the road to digitization greatly aided the oil and gas industries' ability to access data, leading to machine learning easily. The built environment has been the focus of extensive research into intelligent control for air conditioning systems since 2000 to increase the effectiveness of these systems. Artificial intelligence applications in the HVAC sectors are made possible by digitalization, which is essential. Therefore, HVAC firms may create smarter systems to make buildings more environmentally friendly, thanks to technological improvements. Artificial neural networks (ANNs) have also been used in HVAC systems to optimize the operation set points of the air conditioning system.

### **2.1 Review of the application of machine learning (ML) and artificial intelligence (AI) in air conditioning systems**

Many researchers have been working on machine learning and artificial intelligence for both the demand and supply side of HVAC systems. A vast majority of research conducted in the last 10 years can be generally categorized into (i) prediction of occupancy and their behavior, energy consumption, and energy management and (ii) control and optimization of HVAC systems.

Aftab et al. designed and implemented a sophisticated occupancy-predictive control system with the aid of recent development in embedded system technologies [6]. The system is cost-effective, has fewer requirements for powerful processors to execute highly sophisticated tasks, and deploys real-time occupancy recognition using video processing and ML techniques. The model can predict the occupancy pattern and allow to control of HVAC systems using real-time building thermal response simulations, achieving significant energy savings. Reeba et al. developed a model that can determine the occupants' behavior, which generally results in the wastage of energy in the operation of HVAC systems [7]. An ML-based model focused on the space's heat flow and could capture the energy waste depending on the status of the space, such as occupied or non-occupied. The model could predict the optimal temperature settings utilizing the status of the space, along with predicted mean vote (PMV) and the deployment of motion sensors. The author observed that about 50% of the total energy was wasted due to the suboptimal temperature settings in the space. Esrafilian-Najafabadi also analyzed the impact of different occupancy prediction models using ML techniques [8]. Four different ML techniques, namely decision trees, k-nearest neighbor (KNN), multilayer perceptron, and gated recurrent units, were deployed to predict the occupancy types and patterns and provide an accurate and reliable evaluation of the performance of the occupancy model for coupling with HVAC control systems. The author studied different models that analyze the occupants' energy savings and thermal comfort. The study included thermal comfort favored mode and energy savings priority mode. Despite having a trade-off between the occupants' energy savings and thermal comfort, the author observed that equally weighted energy savings and thermal comfort provide the best performance and that the KNN technique outperformed other machine learning techniques. Although numerous studies related to ML techniques that account for occupant patterns and behavior have been conducted, there is a lack of study on effective air distribution due to the dynamics of occupant patterns and their impact on temperature profiles across a spacious open office.

Many researchers emphasize their research on predicting energy consumption and optimizing energy usage by HVAC systems, the most energy-intensive system, utilizing supervised learning methods. For example, Liu et al. applied Deep Deterministic Policy Gradient (DDPG) for short-term energy consumption of HVAC systems [9]. The authors deployed a powerful autoencoder (AE) to process the raw data linked up with the DDPG method to attain high-level space state data for optimizing the

prediction model. In this study, the authors set up a ground source heat pump system (GSHP) to supply a small office's cooling and heating needs. The operation data were used to train the model, and the authors demonstrated the office's energy consumption verification. The authors also verified that the proposed model predicted the state space variables more accurately than the common supervised learning models, such as support vector machine (SVM) and neural network (NN). The rapid expansion of deep learning techniques has made them promising alternatives to conventional data-driven methods. Vazquez-Canteli et al. developed an integrated simulation environment that links the building energy simulators and TensorFlow, which allows the implementation of various advanced machine learning algorithms [10]. This development enables many researchers to test and formulate optimized control algorithms to accommodate potential energy savings in buildings. The simulation platform also can be easily scaled up to the district or city level to study model-free algorithms and their impact on energy consumption and control strategy. Despite many interesting applications of ML and AI in HVAC systems being conducted, some research focused on energy consumption while other emphasized thermal comfort for small offices. There is a gap to close the loop between energy consumption while maintaining the thermal comfort, along with optimized cooling load predictions. In addition, most of the algorithms operate offline and cannot account for the heat loads in space's extremely dynamic nature and external parameters such as weather conditions. In order to incorporate artificial intelligence focused control that enables online load forecasting for extremely dynamic environments, this work is motivated by the desire to investigate the performance of HVAC systems, particularly airside systems.
