Application of Artificial Intelligence in Air Conditioning Systems

Abstract

Urbanization has led to a sharp rise in the demand for power over the past 10 years, alarmingly rising greenhouse gas (GHG) emissions. HVAC (heating, ventilation, and air conditioning) systems account for nearly half of the energy used by buildings, and minimizing the energy use of the HVAC systems is essential. However, the common problems, such as hot spots and cold spots in office spaces, experienced in the building need to be addressed. Therefore, this chapter introduces the application of artificial intelligence proactive control to resolve typical office issues. A demonstration testbed was implemented on the Singapore Institute of Technology (SIT) campus. The experiments were conducted in baseline mode and smart mode. In the case study, two big zones were segregated into 43 micro-zones equipped with smart dampers at each diffuser, allowing a localized set point to improve thermal comfort and eliminate hot and cold spots. It has been observed that the proactive AI control reduces cooling provided to the office by 29 percent and AHU electricity usage by 50 percent, respectively, while keeping the area within thermal comfort range of 23 to 25°C and 50 to 63% relative humidity.

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Chapter
Application of Artificial
Intelligence in Air Conditioning
Systems
Aung Myat
Abstract
Urbanization has led to a sharp rise in the demand for power over the past 10
years, alarmingly rising greenhouse gas (GHG) emissions. HVAC (heating, ventila-
tion, and air conditioning) systems account for nearly half of the energy used by
buildings, and minimizing the energy use of the HVAC systems is essential. However,
the common problems, such as hot spots and cold spots in office spaces, experienced
in the building need to be addressed. Therefore, this chapter introduces the applica-
tion of artificial intelligence proactive control to resolve typical office issues. A dem-
onstration testbed was implemented on the Singapore Institute of Technology (SIT)
campus. The experiments were conducted in baseline mode and smart mode. In the
case study, two big zones were segregated into 43 micro-zones equipped with smart
dampers at each diffuser, allowing a localized set point to improve thermal comfort
and eliminate hot and cold spots. It has been observed that the proactive AI control
reduces cooling provided to the office by 29 percent and AHU electricity usage by 50
percent, respectively, while keeping the area within thermal comfort range of 23 to
25°C and 50 to 63% relative humidity.
Keywords: energy efficiency, all-air systems, airside energy reduction, artificial
intelligence, micro-zones concept, energy savings
1. Introduction
Energy is the most important component for the operation of various sectors,
including transportation, business, residential buildings, and many others. Recent
technological developments have led to a sharp rise in global energy consumption,
which is alarmingly increasing the rate of greenhouse gas emissions. As shown in
Figure 1, the world energy consumption by different sources of fuels was about
173,340 Terra-Watt-Hr (TWh) in 2019, while it was 122,073 TWh in 2000. The
world’s energy consumption increased by approximately 42% within 19 years. Elec-
tricity is the prime energy source that the built environment utilizes. Global elec-
tricity generation in 2021 increases approximately twofold compared to 2000 to
accommodate the drastic increase in energy consumption in the built environment,
as indicated in Figure 2. Primary fuel sources, like coal and gas, account for almost
60% of total primary energy sources, whereas renewable energy makes up only 13%
1

of total primary energy sources. IEA reported that the increase in coal-fired power
plants contributes to a sharp rise in carbon dioxide emissions. The electricity
demand continues to grow by 4% in 2022. Despite substantial expansions of renew-
able energy usage, it is anticipated to offset the rise only partially in electricity
consumption [2]. Due to the rise of greenhouse gas emissions, the environment is
seriously threatened by the continued growth of energy consumption. Authorities
from many countries, however, are focused on achieving net-zero carbon emissions
and a major increase in the production of renewable and clean energy for end
consumers. Figure 3 shows that the total generated capacity will be 38,900 GW in
2050, while the expected rise in electricity output will be roughly 88,000 TWh.
Figure 1.
Energy consumption by different fuel sources since 2000 [1].
Figure 2.
Global electricity generation by sources from 2000 to 2021 [1].
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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 energy-
efficient 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, particu-
larly electricity for the built environment, will rise dramatically unless energy-
saving options and measures are implemented. Moreover, 59% of the world popula-
tion, as shown in Figure 4, resided in highly urbanized regions in 2020 because
Figure 3.
Projected electricity statistics and carbon emissions till 2050 [3].
Figure 4.
World’s Population residing in urban and rural areas [4].
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these regions have employment opportunities, living standards, and ease of com-
mute. After 2007, the proportion of urban residents overtook rural residents,
sharply increasing the need for cooling and heating systems in residential and com-
mercial 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 algo-
rithms, 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, opera-
tions, and forecasting. Machine learning techniques provide the link between the input
parameters and the predicted output variables. Machine learning can be generally cate-
gorized into two groups, namely (i) supervised learning and (ii) unsupervisedlearning.
By deploying the appropriate methods, ML can be applied to the followings:
i. Detecting the sale trends
ii. Time series forecasting
iii. Multivariate time series forecasting with recurrent neural networks (NNs)
iv. Detecting financial fraud using decision trees
v. Convolutional neural networks implementation for car classification
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 intel-
ligence algorithms using historical data because it is easier to acquire thanks to digiti-
zation. 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
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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 intelli-
gence 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 oper-
ation 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 occu-
pancy types and patterns and provide an accurate and reliable evaluation of the per-
formance of the occupancy model for coupling with HVAC control systems. The
author studied different models that analyze the occupants’energy savings and ther-
mal 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 ther-
mal comfort provide the best performance and that the KNN technique outperformed
other machine learning techniques. Although numerous studies related to ML tech-
niques 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, utiliz-
ing 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
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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 con-
sumption 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 perfor-
mance of HVAC systems, particularly airside systems.
3. Case study: deploying AI solution to airside energy efficiency
improvement
3.1 Energy consumption in air conditioning systems
As illustrated in Figure 3, by 2050, worldwide power consumption is expected
to have doubled from what it is today. Although it is questionable whether the
sharp rise in power use is related to the sharp rise in cooling and heating requirements,
the fact that there are currently 1.9 billion air conditioning units worldwide serves as
proof. Additionally, it is anticipated that by 2050, cooling and heating requirements
will have increased by 37%. Therefore, the road map for net-zero carbon buildings
requires immediate effort to increase the efficiency of the air conditioning systems and
the occupants’behavior, incorporate cutting-edge control systems, and embrace pas-
sive technologies. This project aims to increase air conditioning system’s efficiency by
integrating them with AI-focused self-learning control systems.
A case study was carried out at one of the spacious offices at the Singapore
Institute of Technology (SIT) to apply AI to air conditioning systems. Due to its tropical
climate, space cooling is required throughout the year, and the building sector
accounts for 37% of total energy consumption. Figure 5 depicts the energy consump-
tion of the air conditioning system, which is as high as 50% of building energy
consumption due to its hot and humid climate. A detailed breakdown of the energy
consumption of HVAC systems is shown in Figure 5, and airside accounted for 34% of
the total energy consumption of HVAC systems. Although the airside energy con-
sumption is equally important compared to the waterside, it is mostly overlooked due
to the high dynamics in nature.
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3.2 Motivation of the case study
In chiller plants, airside systems account for the second highest energy consumption.
In addition, the airside cannot support more control flexibility due to the high
dynamics involved. Another thing to consider is that uneven thermal heat maps
caused by oversized and undersized air distribution systems deviate from the thermal
comfort of the occupants. Figure 6 shows that hot and cold spots in large open offices
are prevalent issues in airside systems.
Although there are other potential contributing elements, ineffective air distribution
systems are the main problem. Ineffective air distribution systems cause hot areas
because of insufficient cold air provided to the space. In addition, cold spots develop in
the remaining areas of the office because there is excessive cold air. The control system,
however, is unable to respond appropriately. Conventional control systems operate in
Figure 5.
Breakdown of energy consumption of buildings in Singapore [11].
Figure 6.
Uneven thermal heat map due to improper sizing of AHUs and control strategies.
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reactive methods, which is the cause of the control system’s slow response. As indicated
in Figure 7, although the main return air temperature is employed as feedback to serve
as the control, it does not accurately represent the local zones, leading to unequal
temperature distributions. The zoning of the space is one aspect that shows a significant
role. The zone size is too large for the control systems to capture all information; thus,
they are unable to adapt. Therefore, in this study, the impact of the big zone being
segregated as smaller zones (micro-zones), proactive AI control on the performance of
the airside system, and energy savings potential will be investigated.
3.3 Details of the testbed located at the Singapore institute of technology
The pilot tests were conducted on one floor of the Singapore Institute of Technol-
ogy (SIT) campus located at Dover Drive. The testbed occupying 11,000 square feet is
located at level two, comprising open offices, meeting rooms, a pantry, an AHU room,
and washrooms. The space is fully air-conditioned except for the washrooms. The
details of the testbed are tabulated in Table 1.Figure 8 illustrates the layout of the
Figure 7.
Typical reactive feedback control system in air handling units (AHUs).
Total floor area 11,000 square feet (sqft)
Seating capacity Approx. 100
Operation hours 0830 hrs to 1800 hrs
Area types Enclosed workstations, cabins, cafeteria, and conference rooms
BMS Yes, Johnson controls
Chilled water actuator Yes, installed for each AHU
Table 1.
Details of the pilot in the case study.
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office located at the Singapore Institute of Technology, and the spaces are segregated
into two zones, namely, block B and block C. While block B’s cooling requirements are
supplied by air handling unit 2-1 (AHU 2-1), block C is served by AHU 2-2. The
temperature set point of the space was 24°C throughout the day.
The key issue with the air conditioning system is the thermal comfort of the occu-
pants stationed in the space. From the occupant’s feedback, it is discovered that there are
areas with hotspots and overcooling within the office. On occasion, occupants feel
uncomfortably hot or extremely cold in the office. It is observed that some of the
diffusers are covered with masking tape to restrict airflow. The AHU VFD and actuator
set points are changed manually based on complaints from the occupants. In addition,
due to the work nature of the academic staff, they are frequently required to leave and
return to their desks for lectures and classes, resulting in a dynamic heat map. There-
fore, there is a need to resolve the issue without compromising the energy efficiency of
the air conditioning system. The primary objective of this study is to develop an intelli-
gent solution to resolve thermal comfort issues without compromising energy efficiency
while eliminating the conventional reactive approach to control systems.
The proactive solution would account for the varying occupant numbers throughout
the day while creating an optimal condition for their staff. Despite the abundant availabil-
ity of smart sensors, which work on room levels, an AI algorithm was developed and tested
at SIT staff office, along with the collaboration between SIT and Singapore Digital Pte.
Ltd., a sole distributor of 75F smart innovation solutions in Singapore.
Dynamic air balancing and chilled water balancing, along with proactive AI pre-
dictive control, are the essential components of this study to achieve energy savings
while maintaining the thermal comfort of the occupants. In order to optimize the air
distribution efficiency, two big spaces, as indicated in Figure 8, are divided into 43
micro-zones, as indicated in Figure 9. Each meeting room is treated as a micro-zone.
Each micro-zone in the open office is equipped with an IoT smart sensor that mea-
sures the key parameters, such as temperature, relative humidity, CO
2
concentration,
and occupancy status, using a passive infrared sensor (PIR), enabling an accurate
representation of the local heat load. Moreover, as illustrated in Figure 10, smart
Figure 8.
Plan view of the office space at level two of the University Service Centre at the Singapore Institute of Technology.
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Figure 9.
Two zones are split into 43 micro-zones.
Figure 10.
Architecture of proactive AI control system.
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dampers are retrofitted between each supply air diffuser/VAV duct and flexible air
duct to modulate the amount of airflow based on the actual heat load. This facilitates
micro-zonal control, allowing better comfort and energy savings. In addition, the
opening of the smart damper is controlled based on the local heat load. The IoT smart
sensors and dampers are wirelessly connected smart nodes which communicate wire-
lessly with central control units (CCUs). A cloud-based proactive AI control powers
the algorithm behind the control units, and the architecture of the proactive AI control
system is shown in Figure 10. The CCU sends minute-by-minute data regarding
temperatures in various building parts to cloud servers. Every night, these servers run
proprietary algorithms to crunch the historical data and develop a thermal model of
the building. They then predict the thermal load in each part of the building for the
next day based on the forecasted weather.
3.3.1 Dynamic airflow balancing
Figure 11 illustrates the dynamic airflow balancing, which optimizes the cold air
supply to the most required space. The smart dampers’opening at micro-zones with
cold spots is adjusted to accommodate the cooling needs in that micro-zone. Due to the
changes in the opening of the smart dampers, the static pressure in the duct increases.
However, cold air is circulated to space (hotspots), which requires more cooling,
restoring the static pressure. Therefore, supply air fan speed is not ramped up to supply
more cooling to the hot space; instead, air balancing between cold and hotspots pro-
gresses, resulting in energy savings in AHUs. This means that the AI control is able to
identify which zones require more cooling by deploying dynamics zone priority (DP).
Since the system enables minute-by-minute data collection, real-time DP is performed
prior to executing the next control phase. Air balance is performed using a weighted
average of the local heat load, as shown in Eq. (1) (Figure 12).
_
Qweighted ¼Pzn
i¼z1
_
Qi,jxDPi_
Qi,kxDPi

Pzn
i¼z1DPi
(1)
Figure 11.
Illustration of dynamics of airflow balancing.
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where _
Qweighted and _
Qdenote the weighted average of heat load and local heat load in
the space, respectively, DP refers to dynamics zone priority, i denote the number of
zones in the space, j represents the zones with overcooling, and k is for the zones
requiring more cooling. Then, air balancing for the micro-zones is carried out based on
the DP value, which identifies how far the current temperature is away from the set
point. The AI algorithm identifies and optimizes the air balancing, resulting in the evenly
distributed cold air supply to each of the micro-zones, and AHUs can still be operated at a
lower speed as compared to the conventional control system because the speed of the
AHUs is adjusted, as shown in Figure 13, based on the weighted average of micro-zones
after air balancing is carried out. In addition, fresh air optimization is enabled by incor-
porating a modulating damper in the fresh air duct. The bandwidth of the opening of the
fresh air damper ranges from 20 to 100% based on CO
2
concentrationinthespace,
enabling minimal fresh air usage when the indoor CO
2
level is about 900 ppm.
Figure 12.
Dynamic airflow balancing diagram.
Figure 13.
Block diagram that shows smart damper and AHU VFD relational control.
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3.3.2 Dynamic chilled water balancing
Chilled water balancing is achieved by utilizing the micro-zones’weighted
average return air temperature. When the weighted return air temperature falls
above the set point, the Al algorithm detects that more cooling is required in
the space. However, the steps for air balancing and AHUs speed adjustment are
completed to accommodate the cooling requirement. Therefore, the controlled
valve will be modulated to a wider position to provide more chilled water to
maintain the temperature in micro-zones within the thermal comfort range
defined by ASHRAE standard 55 [12]. Therefore, the differential temperature of
the chilled water is maintained at the optimal range, while the chiller water pump’s
speed is adjusted to provide the required cooling in the space, resulting in energy
savings without compromising the thermal comfort of the occupants in the space.
The sequence of activating the opening of the chilled water modulating valve is
shown in Figure 14.
3.4 Deployment of AI solution in airside system of the chiller plant located at SIT
Implementation of measurement of the performance of AI-oriented proactive
control solution comprises the following stages:
1.Retrofitting of smart dampers to the existing system that enables dynamics air
balancing
2.Installation of a power meter at each AHU to measure the power consumption of
the fans
3.Installation of CCU for each AHU to control smart nodes and smart dampers,
modulate VFD and chilled water actuator, and act as a cloud gateway
4.Installation of smart dampers at the existing mixing boxes outlet. The opening
and closing of the smart dampers are controlled by the smart nodes installed
above the false ceiling. The smart nodes communicate wirelessly to the cloud;
users can access all data and control through the App or portal.
Figure 14.
Dynamic chilled water balancing control process.
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5.Installation of Intelligent Temperature Mote (ITM) for each zone across the pilot
area to sense and collect data (temperature, humidity, and Lux) in real-time
every minute, and there are a total of 43 zones, as shown in Figure 15.
6.Installation of chilled water flow meter, the temperature sensors for chilled water
return, and supply.
7.Installation of a chilled water actuator controlled by the CCU to modulate chilled
water flow to match the optimal set point and ensure optimal flow rate and
differential temperature through the chilled water pipe networks, as shown in
Figure 16.
8.Installation of a new fresh air damper with Belimo actuator and the fresh air
damper is modulated (20–100% opening) based on the CO
2
level, as shown in
Figure 17.
3.4.1 Baseline measurement and smart mode measurement deployment of AI solution in the
airside system of the chiller plant located at SIT
After completing the installation of the required instrumentation, sensors, and IoT
devices and commissioning, which includes fine-tuning the parameters, the testbed
was operated in two phases: baseline mode and smart mode. Each mode was operated
for 10 days, excluding weekends. The baseline mode represents the operation of
Figure 15.
A schematic diagram of dynamic airflow balancing.
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existing conditions, isolating the proactive AI control, whereas the smart mode
enables the proactive AI control, including dynamic air balancing, dynamic chilled
water balancing, and fresh air optimization. The AI control overwrites the set point of
BMS for existing operating conditions. Outdoor temperature and relative humidity
were also recorded in the cloud during the testing of both phases to ensure that the
impact of the weather conditions on the airside system’s performance was considered.
During both testing phases, data are recorded every minute using the instrumentation
and sensor installed during the retrofitting stage, as tabulated in Table 2. During
weekdays, AHUs are scheduled to start at 6:30 am, and the chiller and pumps are
staged to turn on from 7 progressively for pre-conditioning. Since the building
operates from 8:30 am to 6:00 pm, the data analysis only includes this period of the
day. Key parameters, such as the temperature and relative humidity of all 43 zones,
were recorded every minute in both baseline and smart modes. While AHU 2-1
supplies the cooling requirements to zone 1–23, AHU2-2 serves zones 24–43. The set
point for all spaces was maintained during the tests at 24°C.
Figure 18 depicts the temperature profiles of supply air measured during the
baseline and smart mode tests. It is indicated that supply air temperature fluctuated
between 13.6°C and 21.7°C, whereas it was maintained between 15.3°C and 19.3°C.
The median temperature of supply air for baseline test and smart modes were 16.3°C
and 17.1°C, respectively. Despite maintaining the close median supply air temperature
between baseline mode and smart mode, the differential temperature in the
interquartile for baseline mode was 1.5°C, and that of smart mode was about 1°C. It is
also concluded from the box plot that most of the supply air temperature during the
Figure 16.
A schematic diagram of dynamic chilled water balancing.
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smart mode test fall outside the interquartile, and outliers are beyond 1.5 times the
interquartile (upper whisker) due to inefficient air distribution systems and control
strategy.
On the other hand, no outliers were discovered beyond the upper and lower whis-
kers during the smart mode testing. It is also worth noting that the temperature
Location Parameters measured
Meeting rooms and office space •Temperature
•Relative humidity
•CO
2
concentration
•Dynamics occupancy
•Supply air temperature from each diffuser
•Smart damper opening in %
AHUs •Fan power
•Fan speed
•Chilled water flow rate
•Chilled water supply and return temperature
•Supply air temperature
•Return air temperature
•Fresh air damper opening in % (20–100%)
Table 2.
List of measured parameters and locations of measurement.
Figure 17.
A schematic diagram of uutside air optimization comfort range.
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difference between the minimum and maximum supply air temperature was less than
4°C, assuring that smart mode control performs significantly better than baseline mode
in terms of air distribution effectiveness. The space temperature with respect to time
during baseline mode and smart mode is presented in Figure 19. During the test period
of both modes, the set point temperature was maintained at 24°C, and the results were
analyzed by comparing the baseline and smart mode tests. From the temperature and
relative humidity profiles during the baseline test, it was observed that the space
temperature during the smart mode test fluctuated from 21 to 25°C, while relative
humidity in the space varied between 67% and 48%. Furthermore, the difference
between space temperature and the set point was found to be considerably huge in
some cases; it was as high as 3°C, resulting in cold spots and hotspots in space. However,
Figure 18.
Supply air temperature profiles during the baseline and smart test.
Figure 19.
Average temperature and relative humidity profiles of the space during the baseline and smart mode tests.
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during the smart mode test, the space temperature varied between 23 and 24.5°C, while
relative humidity ranged from 52 to 65%, which falls well within the thermal.
In order to analyze further details of the temperature distribution in the space, a
temperature bin is created with 2°C range with a total of 551,872 data points, and the
results are illustrated in Figure 20. Seventeen percent of the data points that falls
under undercooled regions (19–22°C) during the baseline test were shifted to 22–26°C
when the smart mode was activated. Moreover, the smart mode delivered 99.97
percent of the events within the bin range of 20–23.9°C, highlighting that proactive AI
control works perfectly fine to optimize the airside performance compared to the
baseline mode. Therefore, proactive AI control not only achieves a better thermal
comfort condition in the space but also improves the efficiency of the airside system,
because AI control optimizes the cooling load prediction by adapting the characteris-
tics and activity ongoing in the space, along with the dynamic airflow balancing
strategy. Energy consumption should not be overlooked despite improving the ther-
mal performance of the airside. Therefore, energy data, such as electricity consump-
tion and cooling supplied to the building, were monitored and recorded throughout
both baseline and smart modes. All energy data were recorded using the Kamstrup
BTU (cooling energy) and the Schneider Energy Meter (electrical energy). Data
during the weekends of the testing period were excluded from the analysis in both
modes. Two AHUs (AHU 2-1 and AHU 2-2) were assigned to supply cooling to the
space, and the rated power of AHU 2-1 and AHU 2-2 at the full load are 5.7 kW and
3.7 kW, respectively. During different test modes, weather conditions were normal-
ized to ensure that the deviation in the weather conditions was not affected. The pairs
of the daily average ambient temperatures during both modes for comparative
analysis are presented in Figure 21.
Daily electricity consumption of both AHU 2-1 and AHU 2-2 is illustrated in
Figure 22. During the baseline test, it is observed that the daily electricity consump-
tion of AHU 2-2 ranges between 44 kWh and 70.50 kWh, while the electricity con-
sumption of AHU 2-1 varies between 22.6 kWh and 8 kWh. While conducting the test
in the smart mode, as indicated in Figure 22, electricity consumption of AHU 2-2
Figure 20.
Temperature distributions with 2°C range.
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fluctuates between 37.2 kWh and 18.6 kW and that of AHU 2-1 peaks at 14.2 kWh,
and its minimum value is 7 kWh. The average electricity consumption of AHU2-1 and
AHU 2-1 during the baseline mode was 54.67 kWh and 16.07 kWh, respectively.
However, the average electricity consumption of both AHUs during the smart mode
was 24.5 kWh [AHU2-1] and 10.43 kWh [AHU 2-2]. Therefore, the total electricity
consumption of AHU 2-2 is cut from 492 kWh in the baseline test to 220.5 kWh in the
smart test, whereas the electricity consumption of AHU 2-1 is lowered by 50.7kWh
from 144.6 kWh to 93.9 kWh, as demonstrated in Figure 23. The results also highlight
that electrical energy savings in AHU 2-2 are about 55%, while AHU 2-1 saves
approximately 35% of electricity usage when the smart mode is activated. While
Figure 21.
The pairs of average daily outdoor temperatures for the comparative analysis during the baseline and smart modes.
Figure 22.
Daily electricity consumption of AHU2-1 and AHU2-2 during the baseline and smart modes.
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Application of Artificial Intelligence in Air Conditioning Systems
DOI: http://dx.doi.org/10.5772/intechopen.107379

presenting electricity consumption analysis, cooling energy consumption is also
investigated in this case study. Due to some constraints in the installation of BTU
meters for each AHU to measure the cooling energy, only one BTU meter was
installed at the common chilled water header to log the chilled water flow rates.
Therefore, the cooling energy consumption (kWh) is calculated as follows:
_
Qcooling ¼_
mchwCpchw TRTS
ðÞxNop (2)
In Eq. (2), the first parameter _
Qcooling represents cooling energy consumption in
kWh; the second parameter _
mchw denotes mass flow rates of chilled water in kg/s, the
third parameter Cpchw is the specific heat capacity of chilled water in kJ/kgK, T
represents temperature in °C, and Nop is the operation time in hours. The subscript R
and S represent return and supply, respectively. Figure 24 illustrates the accumulative
cooling energy consumption for the baseline and smart mode tests. The smart mode is
observed to consume 29% less cooling than the baseline test while maintaining ther-
mal comfort in the space, because the cooling requirements in the office are signifi-
cantly reduced by optimizing the supply airflow rates to facilitate the cooling load in
each micro-zone. The results show that airside energy consumption can be reduced by
as high as 50% of electricity consumption in AHUs, while the reduction in cooling
supply to the office was also approximately 29%. The results also assure that reduction
in the cooling supply and electrical energy consumption do not compromise the
thermal comfort of the office.
This case study demonstrated the application of AI-oriented control in airside air
conditioning systems to resolve typical issues, such as thermal comfort and high
energy consumption due to overcooling and undercooling, in open offices. It also
highlights that the improvement on the airside also contributes to the reduction of
electricity consumption of the fans, resulting in minimizing the waste energy as
compared to the baseline control system while cooling required in the offices is also
optimized.
Figure 23.
Electrical energy savings at different AHUs between baseline and smart modes.
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4. Conclusion
This chapter investigates the application of AI solutions in optimizing airside
performance while maintaining thermal comfort in the office. Pilot tests were
conducted to examine the impact of proactive AI control on resolving common ther-
mal comfort issues in the office, such as overcooling and undercooling. The demon-
stration testbed was implemented in one of the Singapore Institute of Technology
floors, located at Dover, Singapore. The tests were conducted in baseline mode (con-
ventional BMS control) and smart mode (proactive AI control). The results highlight
that the proactive AI control solution provides not only the improvement of energy
consumption but also an enhancement in thermal comfort by eliminating cold spots
and hotspots in the office. Furthermore, it also highlights that the improvement on the
airside also contributes to the reduction of electricity consumption of the fans,
resulting in minimizing the waste energy as compared to the baseline control system,
while cooling required in the offices is also optimized.
Acknowledgements
The author would like to express his gratitude to SP group Pte. Ltd., SP Digital Pte.
Ltd. and 75F solution for providing the financial support to accomplish such a live
testbed on SIT campus.
Figure 24.
Cooling energy consumption during baseline test and smart test.
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Application of Artificial Intelligence in Air Conditioning Systems
DOI: http://dx.doi.org/10.5772/intechopen.107379

Author details
Aung Myat
Singapore Institute of Technology, Singapore, Singapore
*Address all correspondence to: aung.myat@singaporetech.edu.sg
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of
the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited.
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