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IoT-Based Smart Air Conditioning Control for Thermal Comfort

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IoT-Based Smart Air Conditioning Control for
Thermal Comfort
A.M.Ali
1,a
,
S.A.Abdul Shukor
1,b
, N.A.Rahim
1
,
Z.M.Razlan
1
, Z.A.Z.Jamal
2
1
School of Mechatronic Engineering
2
School of Microelectronic Engineering
Universiti Malaysia Perlis
02600, Perlis, Malaysia
a
anwaralgacemy@gmail.com
b
shazmin@unimap.edu.my
K.Kohlhof
Faculty of Information, Media and Electrical Engineering
Cologne University of Applied Science
50679 Cologne, Germany
Abstract—Thermal comfort brings a great influence that
may affect the satisfaction of an occupant about the
surrounding indoor environment, as this could led to the level
of productivity and social interactions. One of the most suitable
tools that have been utilized in tropical region to create
thermally comfortable indoor environment is the air
conditioning (AC) system. However, the conventional method
of controlling the AC may not guarantee thermally
comfortable indoor ambience, furthermore excessive cooling
chosen by users may also contributed to negative side effect
toward the occupancy’s health. To overcome this problem, an
IoT-based smart system that can control the AC to provide a
suitable thermally comfortable environment is proposed in this
project. The system will interact with the users by recording
their feeling toward the environment as the input for the
system. Together with sensors’ data, the information will be
integrated with an enhanced Predicted Mean Vote (PMV)-
based model to control the AC smartly so that the occupant
can feel thermal satisfaction. Based on the results, the IoT-
based smart system can create a thermally comfortable indoor
environment compared to the maximum cooling setting as
usually being adapted by the room’s occupants.
Keywords— Internet-of-Things (IoT), air conditioning system
control, Predicted Mean Vote (PMV)-based model
I. I
NTRODUCTION
In recent years, many researchers around the world have
put more efforts in the field of thermal comfort which is due
to the expanded public argument about the environmental
change. In general, thermal comfort and evaluation of the
indoor environmental quality is depending on human
physiological and psychological responses to the surrounding
environment integrated with behavioural factors that interact
with space [1]. The developing enhancement of the present
employees has made social challenges in a workplace
environment. A specific issue has risen which is on how to
provide a comfortable environment inside an office
throughout the working time [2]. However, obtaining an
optimal office temperature that the worker feel comfortable
with is challenging because it deals with an integration of the
complex thermodynamic of the human body and a nonlinear
mapping between personal preferences and the
environmental variables [3].
Air conditioning systems are adopted as a solution for
thermal comfort purpose indoors and currently are extremely
increasing due to the warmer weather. People in the tropical
area tend to ‘over controlled’ the temperature and the
compressor regulation of the air condition which lead to high
power consumption and it may not provide the satisfied
thermal comfort [4]. As indicated by World Bank, 85% of
the populace will be situated in the developing countries in
2030 [5]. This development is prompting an expansion of
buildings particularly in the downtown (city centre) area. In
return, the usage of artificial system will be gradually
dependent to operate the buildings, as the time in which
people spend inside the building is significantly increased.
This has led architects and engineers to consider approaches
to improve the occupancy’s thermal comfortability by
enhancing the thermal comfort tools in the building, whereas
it is been considered that individuals spend at range from
80% to 90% of their days’ time inside the buildings [6].
Figure 1 summarizes the overall scope of this project.
This study concentrates on tropical region where it faces high
relative humidity, high air temperature and low air speed. It
focuses on small sized room in which the occupancy is doing
sedentary work. Even though there are many tools that able
to create thermal comfort, in this study, AC system will be
considered as the main tool and it will be controlled by using
the enhanced PMV-based algorithm. With the aid of IoT, the
AC can easily and effectively create a suitable thermal
comfort environment for the occupant. This paper is arranged
in this manner: Section II will cover the background that
illustrates the basic ideas of thermal comfort and the selected
enhanced PMV-based model. Section III will cover the
methodology especially on how the integration of the
enhanced PMV-based model with related hardware in
developing the system. Section IV will cover the results, and
Section V will conclude and recommends future work.
II. R
ESEARCH
B
ACKGROUND
A. Thermal Comfort
The word thermal comfort is utilized to imply data about
the thermal condition of human with the indoor environment.
The concept of thermal comfort consists of three main
disciplines such as physiology, psychology and behavioural
factors. Creating a healthy and satisfying condition for the
people is the main objective of thermal comfort researches.
As a whole, indoor thermal comfort researches can be
classified into two approaches: adaptive and steady-state
model. These two approaches have different ideology about
the way individuals react to their surroundings [7].
2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS 2019), 29 June 2019, Selangor, Malaysia
978-1-7281-0784-4/19/$31.00 ©2019 IEEE
289
Figure 1 The scope of this project
The adaptive approach identifies humans as the active
recipient which can react with a response to maintain their
thermal comfort whenever any environment changes
occurred that can cause discomfort to them [8]. According to
Nicol and Humphreys [9], adaptive approach is based on the
studies of naturally ventilated building. While the steady-
state approach is founded by P.O Fanger in 1970s could be
used for indoor environment with AC system [10]. This
approach mainly depends on heat balanced model of the
human body. Predicting a mean thermal sensation of the
occupancy at indoor environment is the main target of the
Fanger’s model. Hence, most of the standard organization
such as International Standard Organization (ISO) 7730 and
American Society of Heating, Refrigerating and Air-
Conditioning Engineers (ASHRAE) use Fanger’s model to
set the requirements for indoor thermal conditions.
1) PMV Thermal Comfort Model
P.O Fanger developed the Predicted Mean Vote (PMV)
by integrating equations of heat balance and experimental
studies about human skin temperature and used it to
determine the thermal comfort. PMV and Predicted
Percentage of Dissatisfied (PPD) are typical indicators that
use to express the thermal sensation of the people. PMV
model is consider as the main reference for the ISO 7730
[11] and ASHRAE 55 [12]. There are six factors that affect
thermal comfort at indoor environment, four of them are
environmental-based such as air velocity, relative humidity,
mean radiant temperature and air temperature while the other
two factors are personal such as clothing rate and metabolic
rate. PMV can be determined by equation (1).
 0.303
.
0.028




 (1)
Where:
M: metabolic rate
W: effective mechanical power
H: dray heat loss
E
c
: evaporation heat exchange at the skin
E
res
respiratory evaporative heat exchange
C
res
respiratory convective heat exchange
PMV deals with a scale from -3 (cool) to +3 (hot), as
appeared in Figure 2. It helps the users in characterizing their
comfortability towards their surrounding environment.
According to ISO 7730 [11], 75% of the people are feel
satisfied with a range between PMV = -1 and PMV = +1.
While the level of people’s satisfaction increased to 90%
with PMV range between PMV = -0.5 and PMV = + 0.5. An
additional PPD index which indicates the relationship of
PMV with the percentage of the people who may not satisfy
with the characterized scale has also been developed.
However, since PPD index is usually being used to measure
dissatisfaction of comfortability in a bigger audience, this
PDD index is not usable here as the enhanced PMV-based
model is designed exclusively for small indoor environment
which occupied one or two people.
2) Enhanced PMV-Based Model
PMV has been used by numerous studies in developing
indoor thermal comfort models, but in this project an
enhanced PMV developed by [13] is adapted. Since the
enhanced model has been developed for the tropical
countries and for environment with occupancy who
performed sedentary tasks, the model utilizes predefined
parameters as expressed in Table I
TABLE I. P
ARAMETERS
R
EQUIRED TO
C
ALCULATE
PMV
AS
D
EFINED BY
ISO
7730
[11]
Parameters Symbol Value
Effective mechanical power W 0 W/m²
Mean radiant temperature tr 25 °C
Person’s activity M 58.15 W/m²
Clothing level Id 0.155 m²°C/W
Turbulence Tu 0%
2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS 2019), 29 June 2019, Selangor, Malaysia
978-1-7281-0784-4/19/$31.00 ©2019 IEEE
290
Figure 2 Seven Point PMV Thermal Sensation Scales
Figure 3 The enhanced PMV used in this project, with the simulation analysis at relative humidity RH = 79.4%
Figure 3 shows the graph representing this enhanced
PMV-based model that has been utilized here. Users can
easily choose a particular PMV scale ranging from -1 to +1
with 0.5 increment or decrement as desired and the model
will set a fitting estimation of the air temperature and air
velocity (or fan speed) of the air conditioning system to
develop a thermally comfortable environment. In this study,
the analysis are done at an office room in the School of
Mechatronic Engineering, Universiti Malaysia Perlis
(UniMAP) which is located in Pauh, Perlis, Malaysia where
its annual average relative humidity (RH) = 79.4% according
to Malaysia Meteorological Department [14]. As shown in
Table II, if the user selects PMV = -1, then the value of AC
air temperature (T°) and fan speed (v) of the AC should
operate is around 17°C and 0.180m/s (low) respectively.
While if the user choose PMV = -0.5, then T° = 19°C, v =
0.212m/s (low). Meanwhile, for PMV = 0, then T° = 21°C, v
= 0.255m/s (medium). And for PMV = +0.5, then T° = 23°C,
v = 0.310m/s (high); PMV = +1, then T° = 24°C, v =
0.360m/s (high).
TABLE II. V
ALUE OF THE
AC
AIR TEMPERATURE AND FAN SPEED
FOR
PMV
VALUES WHEN
RH=
79%
BY
[13]
PMV value AC air temperature AC fan speed
-1 17°C Low
-0.5 19°C Low
0 21°C Meduim
+0.5 23°C High
+1 24°C High
2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS 2019), 29 June 2019, Selangor, Malaysia
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291
B. AC system as the thermal comfort tool
1) Usage of conventional AC system to create thermal
comfort
AC system is an electrical machine that is used to
improve the comfort level of an indoor environment by
controlling the air temperature of that closed area. AC
consists of inner and outer units and using condensation and
evaporation properties of the refrigerant gas, it does cooling
and heating function. According to Ozkan and Aybar [15],
by operating the compressor as on/off, AC attempts to
maintain room temperature within a range of temperature
value that has been set by the user. For instance, during
summer season when the room temperature is higher, user
will set a cooling temperature (Tc), and the compressor will
operate to blow cool air until ambient temperature reaches
Tc. The compressor will stop when room temperature
exceeds Tc. The inner fan will keep on working in order to
maintain the air flow within the room. If the cooling
parameters are measured properly, the AC system could be
used to control the room's air draft to generate a thermally
comfortable condition in the room [13].
Noticeably, user tends to set the AC system into
maximum cooling (lowest temperature at highest fan speed)
especially when he / she comes from a warmer place.
However, this will not guarantee a thermally comfortable
environment for the occupant, and furthermore, it can
increase the energy utilization of air conditioning system
[16]. In few nations, ‘reduce thermostat temperature setting’
becomes one of the inspiring campaigns. For example, in
Malaysia, minimum thermostat temperature of 24 °C for
cooling governmental offices has been promoted by the
government [17]. Instructions on the control of indoor
regulator (thermostat) setting are particularly important for
developing nations located in a tropical area as reviewed by
Damiati et al. [18]. However, people are not following the
instruction of thermostat, especially when occupant arrived
from outside where high solar radiation and high relative
humidity at daytime tend to utilize excessive cooling which
as a result it may led them to discomfort and illness for
prolong usage.
2) AC control using IoT-Based
Thermal comfort has been exposed to enhance the
pleasure of workers as well as their efficiency at work and
their social collaborations. Many researchers suggested an
Internet of Thing (IoT)-based system to control AC system
so as to create a thermally comfortable environment with low
power consumption. Project by Song, Feng, Tian, & Fong,
[19] proposed an IoT-based controlling system for the air
conditioner to reduce the power utilization, the system
concentrate on energy reducing rather than thermal
comfortability of the occupancy and their project have lack
of standard algorithm such as PMV.
In an experiment that was done by Godo, Haase, & Nishi
[20], effective air conditioner control was achieved by means
of self-controlled sensor (pressure sensor) which are fitted in
seats and can sense whenever individual takes a seat or stand
up. The data from wireless sensor network (temperature and
humidity sensor) and pressure sensor are administered using
a cloud server which allow the smart system to regulate the
air conditioner function accordingly. The system can be
fitted into current residential house. All through
experimentation, utilization of electricity was decreased.
Although the results emphasis decreasing on the total energy
usage, electricity demand increase when more people enter
the room. Furthermore, since their system set the PMV at
specific value, this may not provide a thermally comfortable
ambiance as desired.
III. S
MART
AC
C
ONTROL
The structure of the proposed smart system is as shown in
Figure 4, involves of hardware platform and programming
algorithm in suitable software. DHT11 sensor, Esp8266
NodeMcu Wi-Fi microcontroller, relay circuit and IR
remote, smart phone and 1HP portable AC are part of the
hardware used in this smart system. The software includes
the programming of the air temperature and relative humidity
DHT11 using Arduino. When the user insert his / her thermal
feeling (PVM preference) from range (-1 to +1), the mobile
application will communicate with Esp8266 NodeMcu Wi-Fi
for sending data to the cloud server to control the AC as
desired.
Figure 4 Structure of the proposed smart AC system
The collected data of the sensors must pass through a
processing stage in order to effectively control the AC and
achieve satisfied thermal comfort. The most essential part of
the data processing is designing a suitable programming of
Android Application by utilizing enhanced PMV-based
model. Arduino software is used to program the system
which have three input variables which are users’ feeling or
user PMV preference at range of -1 to +1, relative humidity
at range from 40% to 90%, and room temperature at range
from 19°C to 34°C. Two output variables which are
temperature of the AC (from 17°C to 26°C) and fan speed
at range from 0.1m/s to 0.5m/s are used.
IV. R
ESULTS AND
D
ISCUSSION
Creating a comfortable environment is very significant in
order to enhance personal satisfaction as well as efficiency of
work. The proposed smart system will monitor the air
temperature and relative humidity of the room using DHT11
sensor. A mobile application which acts as a controller
allows the user to control the AC system in creating a
thermally comfortable ambiance. The graphical user
interface (GUI) allows the user to insert his/her feeling. The
integration of the user feeling together with the enhanced
PMV index relation is as shown in Table III. If the user feels
cool in the room then the PMV index should be assigned to
warm, and if the user feel slightly cool then the PMV will
2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS 2019), 29 June 2019, Selangor, Malaysia
978-1-7281-0784-4/19/$31.00 ©2019 IEEE
292
assigned to slightly warm. However, if the user feels
comfortable with the environment then he/she can choose
neutral and the PMV sensation will be neutral.
This mobile application system would be more effective
to be used than utilizing conventional remote to control the
AC system. This is because conventionally, users will try to
use the AC system in maximum cooling by setting the AC
temperature to the lowest value and at high fan speed [4]
[16]. Moreover, the value of the temperature may not
represent the desired thermally comfortable environment.
Furthermore, the mobile application implemented in this
project is designed according to the enhanced PMV-based
model which is developed based on standards such as ISO
7730 and ASHRAE 55. A corresponding value of the AC
temperature and fan speed will be displayed at the GUI for
users’ convenience. In terms of the security and privacy, the
mobile application is fully secured because it is designed
using MIT App inventor and there is no users’ personal data
needs to be uploaded on the internet, i.e. ThingSpeak website
[21]. The only data needs to be uploaded are users’ feeling
toward their environment, air temperature and relative
humidity inside the room.
TABLE III. R
ELATIONS BETWEEN USERS FEEELING AND
PMV
THERMAL SENSATION
Users feeling PMV sensation PMV Value
Cool Warm -1
Slightly cool Slightly warm -0.5
Neutral Neutral 0
Slightly warm Slighly cool +0.5
Warm Cool +1
Figure 5 and 6 show the reading of air temperature and
relative humidity inside the room, the data collected using
DHT11 and sent to the internet cloud using ThingSpeak
website that act as communication platform between the
Esp8266 NodeMcu Wi-Fi microcontroller and the Android-
based application. It has been cleared from Figure 5 the air
temperature of the room when the smart system applied
became at the range of 23°C to 25°C (PMV= -0.5 and
PMV= 0) which consider the comfortable zone according to
[22]. Meanwhile, Figure 6 shows that value of relative
humidity varies from 83% to 93% as expected due to the
area of our study is located nearby Chuping, Perlis, Malaysia
which is known to be high in relative humidity according to
Malaysia Meteorological Department [14].
Figure 5 Air temperature reading taken by DHT11 sensor
Figure 6 Relative humidity measurement taken by DHT11 sensor
Figure 7 shows the GUI of the Android-based application
for AC control. The GUI is classified into three main parts:
the first section is for displaying the current relative
humidity and air temperature inside the office room. While
the second section is for the users’ feeling about the
environment and consists of five button – cool, slightly cool,
natural, slightly warm, and warm, so that the occupant can
choose any button according to his/her feelings. However,
when the temperature of the room is below 20°C, and if the
user selects ‘feel warm’, the system will be at neutral mood
to avoid excessive cooling. The third section in the GUI is
assigned to display the corresponding value of the
temperature (from 17°C to 26°C) and fan speed (low,
medium, high) that the AC should work according the user’s
feeling.
Figure 7 GUI of the Android-based Application for AC control
V. C
ONCLUSION
The main contribution of this project is to propose an
2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS 2019), 29 June 2019, Selangor, Malaysia
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293
IoT-based smart system that able to control the AC in order
to produce an appropriate, thermally comfortable indoor
environment by monitoring air temperature and relative
humidity inside the office room. It has been realized that
most of the occupancy discomfort is resulting from the
method of controlling the AC. Working the AC at maximum
cooling for an extended time may affect the occupancy’s
health and welfare. Therefore, by applying the developed
IoT-based smart AC controller with the selected enhanced
PMV-based model, the AC can create a comfortable
environment that can increase user’s satisfaction about the
environment and this will have a great impact of the
workers’ productivity. The system could be improved by
integrating the artificial intelligent element and be tested in
a more variety of rooms and conditions, with respect to
different type of AC.
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In today’s world, energy efficiency is the most sought characteristic. Enhancing energy efficiency and hence moving towards sustainability is considered as the primary requirement. Buildings are the places where we spend most of our time and thus they consume more than one-third of the energy consumption. This forces the stakeholders to think about the new ways to strike a dynamic balance between comfortable human dwelling, efficient resource management, and protecting our environment. Uncontrolled heat and moisture transport impact the building physics, occupant’s health and comfort, and energy efficiency significantly. This can be controlled by modeling the nonlinear and complex hygrothermal behaviour of the structures in an early phase. Several researchers are working to improve the hygrothermal dynamics of buildings which makes it necessary to succinctly review the progress in the field. The present communication signifies the study to assess the hygrothermal dynamics by considering the role of building materials and ventilation systems for improving the building hygrothermal characteristics and thus making it more energy-efficient. The CIMO and PRISMA approaches have been utilized to synthesize the literature comprehensively. The results reported that building performance is subjected to appropriate hygrothermal dynamics which can be optimized by selecting the less sensitive, sustainable, and heat resistant building materials in the pre-construction phase. Also, utilizing appropriate ventilation can also aid in improving the hygrothermal dynamics. Finally, this article can also be considered as a benchmark for modern professionals (e.g. designers, energy auditors, researchers, conservators, buildings’ owners, and policymakers) and can drive them towards suitable and reliable retrofit and maintenance interventions by considering the effect of hygrothermal dynamics.
... Air-conditioning (AC) system is one of the tools that can be used to control the thermal comfort environment indoors [19]. The usage of AC nowadays increased significantly, especially in hot and humid countries [13]. ...
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This paper discusses the comparison of two methods to achieve thermal comfort utilising air conditioning (AC) system in a small indoor space – adaptive control and fuzzy control. Thermal comfort indoors is performed to provide comfortability individually or for a group of people. Due to the small indoor space which usually a bit cramped, crowded and less airy, the ambience can be very uncomfortable either for doing sedentary or active work, thus the AC system can be very useful to provide thermal comfort. Both methods can be utilised depending on how thermal comfort is viewed and how the level of thermal comfort is decided. Every method has its own advantage and limitations, and will be covered in this paper as well.
Chapter
The proper functioning of higher education institutions depends on multiple factors. The teaching-learning process is essential, which turns professors and students into leading actors. However, there are many other factors that must be considered. A key element is the quality and comfort of the air (including temperature, degree of humidity, and purity). This aspect is related to two issues of great importance that are energy efficiency and indoor pollution. In this chapter, the authors want to make visible the importance of maintaining adequate air quality and comfort in the buildings of higher education institutions, not only to achieve energy efficiency and consequent cost reduction, but also to improve the well-being and health of the entire university community by reducing, as much as possible, air pollutants in university campus buildings. In addition, the main emerging technologies available to achieve this objective, such as artificial intelligence, big data, internet of things, or edge computing, will be presented together with practical use cases.
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The internet of things connects objects to the internet, enabling the dialogue between devices and users, providing new opportunities for applications, such as thermal comfort. In the research, adequate sensors were used to measure the heat index, the thermal discomfort index and the temperature and humidity index based on the temperature and relative humidity of a remote indoor environment. This research evaluated the level of thermal comfort in real-time using tools of storage, processing and analysis of big data information from the collection of IoT devices. With the analysis of the environment, it is possible to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms were also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment. Health researchers, for example, have the necessary knowledge to evaluate clinical data, but they are not used to using data analysis resources and machine learning algorithms. The platform was developed to reduce dependence on data experts and encourage healthcare researchers to develop their own models by automating the steps required for model development, using automated machine learning (AutoML).
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Thermal comfort in office buildings is emerging as an important ariable that can be used to maximize employee productivity. In this paper we propose a new Internet of Things (IoT) based system that creates a personalized model of thermal comfort. To create this model, our system collects telemetry via an IoT network of sensors and user inputs. This data is then input into machine learning algorithms that continuously calibrate and update a personalized thermal comfort model for the user. To facilitate the individuality of our models, the system combines personal measurements from the Microsoft Band, such as biometric readings and user feedback, with environmental measurements such as temperature, humidity, and air speed. In this work, we evaluate a broad set of classification and regression algorithms. Our experimental results show that using our IoT based system improves the mean squared error of the thermal prediction by about 50% when compared to the industry standard method developed by P.O. Fanger.
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Building management system has ability to control buildings electrical and mechanical equipment namely heating, cooling and ventilating. Building management system can also provide indoor thermal comfort within residential buildings including institutional, commercial and industrial buildings and able to reduce energy consumption. However most of heating, ventilating, and air conditioning (HVAC) systems are controlled by using conventional controllers whose functions are based proportional integral derivative controllers. This controller is not the ultimate solution to save energy because the operations of HVAC systems are nonlinear. Thus, the implementation of fuzzy logic, neuro-fuzzy, fuzzy PID, neural network and genetic algorithm controllers within smart buildings will be more efficient which consequently will save more energy. This paper reviews different methods of control systems for energy and comfort in buildings. Additionally, it highlights the recent developments in building management system controllers including its conceptual basis, limitations and capabilities.
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The usage of air conditioning system to create a thermally comfortable environment in tropical countries is often a must, not necessarily luxurious any longer. However, the extreme usage of the system will lead towards higher consumption of energy and higher cost. A promising energy efficient model developed based on the Predictive Mean Vote (PMV) is analyzed and evaluated here to distinguish its workability. This model only requires the user to enter their respective PMV value (from -1 to +1) and the respective parameters will be inserted into the air conditioning system, which is based on the standard thermal comfort ISO 7730. Analyses and evaluations were done based on the measurements from human subjects and their feelings towards the surroundings were recorded to see the performance of the model. From here, more than 91% of the subjects agree with the parameters used in defining their thermal comfort. This proves the workability of the model towards controlling the air conditioning system in creating a thermal comfort ambience at lower energy consumption, and further simulative investigations are appreciated before implementation.
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This study examines the level of the thermal environment in residential building in Malaysia at various urban micro-climates which are Kuala Lumpur (KL), Bayan Lepas (BL) and Kuching (K). A simulation model of a typically-designed residential building in Malaysia was constructed to determine the indoor temperatures in the indoor living spaces, namely living cum dining area (DL), wet kitchen (K) and master bedroom (MB). Findings suggest that the highest indoor temperature of 32.6 °C was obtained in a master bedroom (MB) in under KL climate at 1400 hours. Overall, indoor temperatures are higher than the indoor design conditions recommended in Malaysian Standard; MS1525:2007.
Conference Paper
In this paper, an effective low-cost heating, ventilation, and air conditioning (HVAC) control system incorporating a new sensor is proposed. This research builds upon our previous system, which achieved efficient control of the air conditioning (AC) for a large room in a library. By detecting whether or not individual chairs were occupied and regulating the AC near unoccupied chairs, the system reduced the overall power consumption. This detection was achieved using pressure sensors installed in the chairs. However, the sensors required battery power to measure voltage and transmit data. The batteries needed to be replaced within several months, and thus the system required frequent maintenance. In this paper, we introduce a self-powered sensor. The sensor integrates an energy-harvesting switch and a wireless transmitter. The action of pressing or releasing the switch generates sufficient electricity to power the device, and this enables long-term operation without replacing or charging the batteries. When these sensors are integrated into chairs, occupancy status can be detected without additional power or regular maintenance. The proposed improved system with battery-free chair sensors is evaluated in an eight-day experiment conducted at a library in Kurihara, Miyagi Prefecture, Japan. The energy efficiency evaluation indicates that the proposed system can reduce electricity consumption by 18.3%. The predicted mean vote (PMV) values for the environment in the library are determined to assess the comfort level, and these values confirm that the proposed system is capable of maintaining occupant comfort. In the experiment, wireless sensor network is built through the Library to the Laboratory. All sensing data (e.g. temperature, humidity occupancy, status, power consumption, air conditioning status) are managed on the Cloud.
Book
The fundamental function of buildings is to provide safe and healthy shelter. For the fortunate they also provide comfort and delight. In the twentieth century comfort became a 'product' produced by machines and run on cheap energy. In a world where fossil fuels are becoming ever scarcer and more expensive, and the climate more extreme, the challenge of designing comfortable buildings today requires a new approach. This timely book is the first in a trilogy from leaders in the field which will provide just that. It explains, in a clear and comprehensible manner, how we stay comfortable by using our bodies, minds, buildings and their systems to adapt to indoor and outdoor conditions which change with the weather and the climate. The book is in two sections. The first introduces the principles on which the theory of adaptive thermal comfort is based. The second explains how to use field studies to measure thermal comfort in practice and to analyze the data gathered. Architects have gradually passed responsibility for building performance to service engineers who are largely trained to see comfort as the 'product', designed using simplistic comfort models. The result has contributed to a shift to buildings that use ever more energy. A growing international consensus now calls for low-energy buildings. This means designers must first produce robust, passive structures that provide occupants with many opportunities to make changes to suit their environmental needs. Ventilation using free, natural energy should be preferred and mechanical conditioning only used when the climate demands it. This book outlines the theory of adaptive thermal comfort that is essential to understand and inform such building designs. This book should be required reading for all students, teachers and practitioners of architecture, building engineering and management - for all who have a role in producing, and occupying, twenty-first century adaptive, low-carbon, comfortable buildings. © 2012 Fergus Nicol, Michael Humphreys and Susan Roaf. All rights reserved.
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The aim of this paper is to review the literature on human thermal comfort in the built environment. First an overview about the subject area is presented. This is followed by a review of papers published in the last 10 years that examine the various sub-areas of research related to human thermal comfort. Some remarkable works about both the Fanger's and adaptive thermal comfort models are also discussed. This review does not contain simulation works and/or experimental studies without subjective results of people. As a result of the literature review, 466 articles were classified and grouped to form the body of this article. The article examines standards, indoor experiments in controlled environments (climate chamber) and semi-controlled environments, indoor field studies in educational, office, residential and other building types, productivity, human physiological models, outdoor and semi-outdoor field studies. Several research topics are also addressed involving naturally ventilated, air-conditioned and mixed-mode buildings, personalized conditioning systems and the influence of personal (age, weight, gender, thermal history) and environmental (controls, layout, air movement, humidity, among others) variables on thermal comfort.