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Article

Triangulation Method to Assess Indoor Environmental Conditions and Occupant Comfort and Productivity towards Low Energy Buildings in Malaysia

1
Department of Architecture, Faculty of Built Environment, Universiti Malaya, Kuala Lumpur 50603, Malaysia
2
School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia
3
Department of the Natural and Built Environment, College of Social Sciences and Arts, Sheffield Hallam University, Sheffield S1 1WB, UK
*
Author to whom correspondence should be addressed.
Buildings 2022, 12(11), 1788; https://doi.org/10.3390/buildings12111788
Received: 11 August 2022 / Revised: 25 September 2022 / Accepted: 30 September 2022 / Published: 25 October 2022
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)

Abstract

:
Saving energy and cutting costs without compromising indoor comfort conditions are challenging, especially in hot and humid regions such as Malaysia. This study explores a new approach to reducing energy consumption without compromising staff comfort in office buildings. This study aims to develop a method for lowering Building Energy Index (BEI) and maintaining acceptable indoor conditions while increasing productivity in office buildings. A developed triangulation method using Building Use Studies (BUS) for evaluating occupant satisfaction, physical measurements, and simulation modelling was implemented to measure indoor performance in an office building. The results indicated that enhancing six variables of building conditions managed to improve occupant satisfaction by 44%. Hence, the productivity of staff in the building increased by 16%. The findings demonstrated that a reduction of 3 h in the operating times of chillers while an increase in chillers’ temperature by 1.5 °C maintained an acceptable indoor environment and reduced the building’s BEI to 89.48 kWh/m2/year, with an energy saving of 21.51%, turning the case study into a low energy building.

1. Introduction

Global environmental issues require further investigations to reduce carbon emissions and energy consumption in the built environment. The concept of net zero buildings is relatively new and has been generally introduced in the last decade. More efforts have been exerted to enhance existing buildings to use less energy [1]. Sustainable buildings can be developed to achieve zero or near zero energy by utilizing passive, active, or hybrid interventions. In tropical buildings such as Malaysia, studies have indicated that most energy is used to control air temperature, provide dehumidification, and increase air circulation [2]. However, it is not simple to lower energy while maintaining comfortable indoor environmental conditions [3]. Holopainen et al. [4] corroborated the notion by describing a comfortable environment as achieving satisfactory indoor conditions while maintaining an optimum use of energy and running costs as ways to promote good dynamics and high performance for its occupants. On the other hand, the influence of thermal comfort on occupants’ working conditions, such as productivity, has been studied extensively in the literature [5,6,7]. Some studies showed that enhancing indoor thermal conditions could save several billion dollars annually by saving energy usage in building operations and maintenance [8].
The World Green Building Council [9] defines a net zero carbon building (NZCB) as a building that is highly energy efficient and fully powered by on-site and/or off-site renewable energy sources. On the other hand, several researchers have described NZCB as an approach that can be achieved by decreasing energy needs through the productivity of occupants [10]. Strategies of passive energy design towards zero fossil energy goals have been studied since the mid-twentieth century [11]. However, it is challenging to reduce energy without compromising indoor conditions, especially in hot and humid regions such as Malaysia. In such an environment, heat stress represents a severe issue [12,13].
Hot and humid conditions in working places may lead to heat-related side effects or sicknesses such as overwhelming sweating, lack of hydration, low circulatory strain, and salt lopsidedness, which promote sharp muscle agony [5,14]. As employees spend most of their day in office spaces, indoor environmental conditions play an important role in providing comfort and satisfaction. The comfort conditions have a direct influence on employees’ performance [15,16]. There is an immediate connection between the indoor comfort factors of enclosed spaces and the productivity of the users [17].
Previous studies demonstrated the effects of workplace conditions on the working environment [5,6,7,18]. The comfort level in the workplace is influenced by different factors, such as the physical properties of building materials, ventilation, and space usage. Individuals or occupants play a critical role in building energy consumption. Occupants use different cooling or heating mechanisms to accomplish comfort. Kofoworola and Gheewala [19] indicated that the energy consumption rates are high during working hours. Fisk et al. [8] stated that every year 17 to 26 billion dollars in monetary benefits are achievable in office buildings by enhancing indoor thermal conditions in the United States.
Thermal comfort has been defined by Hensen [20] as “a state in which there are no driving impulses to correct the environment by the behaviour”. The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) [21] defined it as “the condition of the mind in which satisfaction is expressed with the thermal environment”. As such, it is influenced by personal differences in mood [22], culture [23] and other individuals, and organizational and social factors [24]. Based on the above definitions, we can perceive comfort as a state of mind.
Comfort has a strong influence on occupant productivity. Occupants that reported complaints of thermal discomfort demonstrated low productivity [25,26,27]. Studies indicated that thermal condition is critical for occupant productivity. According to Vimalanathan and Babu [28], an office environment has a range of purposes, such as reading, typing, and learning activities. Air temperatures from 18 °C to 30 °C demonstrated a diverse response in occupant productivity. In an office environment, temperatures between 21 °C and 25 °C represent the optimum range to provide a sense of comfort. If the air temperature rises above 25 °C, an increase of 1 °C reported a 2% decrease in productivity up to 30 °C [16,29].
Most buildings in the tropics still rely on conventional energy sources, accounting for some 45% of carbon emissions. In addition, it has been estimated that 80% of new buildings to be occupied in 2050 have already been built [30]. Therefore, further efforts to enhance existing buildings based on NZCB should be considered in the tropics. Notwithstanding, providing a clear pathway to enable relevant stakeholders to effectively tackle the complex phenomenon and significantly reduce carbon emissions in the construction industry is lacking [31]. However, more efforts are needed to develop effective methods to reduce energy consumption while maintaining high levels of comfort and productivity. As a result, the adopted methods must be researched and perfected to quickly address existing buildings to mitigate climate change.
The aim of this research is to develop a method for lowering Building Energy Index (BEI) and maintaining acceptable indoor conditions while increasing productivity in Malaysian office buildings. The originality of this study lies in developing a triangulation method using Building Use Studies (BUS) for evaluating and benchmarking occupant satisfaction, physical measurements, and simulation modelling to configure the indoor environmental performance of an office building. The core of this study is maintaining (or increasing) staff’s productivity while decreasing the Building Energy Index (BEI). In the tropics, this approach would help to achieve energy reduction without compromising staff satisfaction in office buildings.

2. Materials and Methods

A developed triangulation method using Building Use Studies (BUS) for evaluating occupant satisfaction, physical measurements, and simulation modelling was utilized to measure building performance. A case study building with standard performance was selected for an in-depth study through all its aspects as shown in Figure 1. The primary data were collected with the BUS survey, which is used to indicate the occupants’ perception of indoor environmental conditions. Simultaneously, the actual indoor environments were physically measured by HOBOs data loggers (Onset Computer Corporation, Bourne, MA, USA) to acquire the indoor performance. Finally, IES-VE for education software (Integrated Environmental Solutions Limited, Glasgow, UK) was used in modelling the case study; this method was used for validation purposes to assess simulation outputs against measurement results.
The primary data were analyzed using four different fundamental analyses. For the BUS survey variables, SPSS (IBM, Armonk, NY, USA) hierarchical multiple regression and BUS benchmarking were applied for analysis. At the same time, the physical measurements were evaluated using HOBOware graphing & analysis software (Onset Computer Corporation). For simulation, indoor environmental conditions were presented using descriptive statistics to quantitatively summarize the generated outputs. In addition, IES-VE was used to simulate several case scenarios with indoor environment enhancements following the variables indicated by the BUS survey to achieve the lowest BEI results without interfering with comfort and productivity, as shown in Figure 2.
The research used a case study to prove and analyze the various phenomena that constitute a particular case [32]. According to Yin [33], the research could have a single case study if it is exploratory and involves more than one analysis unit. Thus, even though this research uses only one case study, different types of data and analyses were applied to fully cover this case study from different perspectives. The selected case study is the Universiti Malaya Chancellery Building in Kuala Lumpur, Malaysia. It is an office building in which different administrative divisions of 400 staff members are centralized. The building was completed in early 2011 and has been fully operational since mid-2011. The building represents the fusion of modern and traditional architecture, in which the designer mixed the use of curtain walling (modern element) and latticework with Islamic motifs (traditional) in the façade. In keeping with the popular “Malaysian Architecture” style of the 80s, the building was painted white and became the new icon for the university. Driving along the middle ring road of the campus, the building sits symbolically on the horizon of a lake as shown in Figure 3.
This nine-floor building was initially designed using an open plan concept with central and side cores. Eventually, the open plan office layout evolved into a semi-open plan (closed plan) due to the specific needs of the administrative divisions. As one of the latest additions, the study building is expected to provide a comfortable indoor environment for its occupants with shading elements incorporated into the building façade design. The eleven-story building houses administrative offices, meeting and seminar rooms, an art gallery, and a sub-basement car park. Designed and built to take advantage of its location adjacent to Varsity Lake, the building was furnished with floor-to-ceiling glazing walls on its most prominent facades, allowing for a fantastic view of the lake and the old Chancellery.
This building is located in a greenery area with low building density and perfect north and south orientation. With the view, the glazing invites natural light, but also glare and heat from the morning and evening sun. The fully air-conditioned building was designed with a central core for lifts, risers for services, and emergency staircases, which prevent the use of natural ventilation. However, the toilets, pantry, air handling unit (AHU) rooms, and another set of emergency staircases were positioned on both shorter ends of the rectangular building where natural ventilation is possible and fully utilized. This building was chosen as a case study to demonstrate how an existing office can be evaluated for reducing energy consumption while maintaining an acceptable working environment.
Due to its administrative purpose, the usage of the building is entirely predictable. Activities are expected to generally commence at 8 am and subside at 6 pm from Monday to Friday. Minimal activity is expected during Saturday, Sunday, and public holidays. One hundred per cent of the energy consumed by the study building is in the form of electricity supplied by the national electricity provider. According to the monthly electricity use reports, the average total electricity usage for this building for three months was 180,500 kWh. Based on calculations, the yearly energy consumption for the building is about 2,166,000 kWh, which means the BEI is 114 kWh/m2/year. Table 1 summarizes the methodology applied in this research.
The first method used in this study is the BUS technique, which is a global and large-scale study initiated in 1985 [34] to study building performances [35]. Besides providing a benchmark dataset for the study, BUS also provides a rapid and thorough analysis method for buildings studied. Another advantage of adopting this survey is that the BUS approach has developed a database of all studied buildings and created ‘benchmarks’ based on the most recent fifty buildings surveyed from 17 countries [36].
Measuring satisfaction and comfort is a subjective exercise. It has been argued that occupants’ satisfaction and comfort are subjective variables that can be misleading and biased if measured inappropriately and poorly supported by evidence [37]. Satisfaction may be defined differently from one respondent to another. Where an individual respondent may feel ‘satisfied’ when the building and its facilities are all in working condition, another may feel ‘satisfied’ only if he/she feels that they deliver more when working inside the building. Therefore, the questionnaire was constructed from many sub-measurements that describe ‘satisfaction’ and ‘comfort’ when aggregated.
The second approach in the developed triangulation method is the empirical measurements using HOBOs data loggers. These devices were used to collect data about the building’s internal conditions, including air temperature, relative humidity, and illuminance. The measurement took 7 days covering weekdays and weekends for each floor from the first week of December 2016 to the end of January 2017. Eight data loggers were installed on each of the selected six building floors, namely 2nd, 3rd, 6th, 7th, 8th, and 9th. The HOBOs were not installed on all the building floors as some floors were unoccupied during the study or used for different functions (e.g., gallery or car park), and other floors have temporary occupations such as the 4th floor, which include meeting rooms and a prayer room. The HOBOs were installed at 900 mm above the floor, within the average seating height in an office, carefully considering their locations in the office spaces and considering the existence of two AHUs on each floor. Typically, four data loggers were distributed under each section of the AHUs, as shown in Figure 4.
The third approach in the developed triangulation method is simulation modelling using the Integrated Environmental Solutions-Virtual Environment (IES-VE). IES-VE represents one of the most reliable simulation tools in energy-efficient design, particularly for building systems. IES-VE meets the requirements of ASHRAE Standard 140 and CIBSE AM11 [38]. The Green Building Index [39] and the Building Energy Efficiency Technical Guideline for Passive Design [40] recommend the simulation program for Malaysian conditions. Several studies have validated the accuracy of the selected software, and a procedure commonly referred to as calibration was performed on the simulation model. The findings of the simulations by Al-Tamimi and Syed Fadzil [41], Lim and Ahmad [42], and Al-Obaidi et al. [43] investigated the accuracy of IES-VE with field measurements. The results of IES-VE evaluation, including solar radiation (irradiance and irradiation), air temperature, and air humidity, showed a high-reliability level in the tropics.
This study is limited to measuring the indoor air temperature, relative humidity, air velocity, and lighting due to equipment availability, plus the ability to configure a comfortable indoor environmental condition without restricting the occupants’ clothing. The clothing restriction issue stems from the need to adapt to the hot and humid tropical climatic conditions where the case study is located. Further, the clothing of occupants differs between genders and staff ranks. Thermal comfort was not conducted in the field and simulation analysis. Therefore, the satisfaction of indoor conditions was instead examined using the BUS survey instead of a PMV-PPD model. In addition, a comparison with the PMV-PPD was not made as the PMV method has low accuracy for air-conditioned buildings, which affects the accuracy of the PPD, as argued by Cheung et al. [44].

3. Results and Discussion

The total number of participants who answered the questionnaire survey was 208 out of 376 (56.7% response rate). The participants were from twenty different departments on nine floors within the building. The demographic characteristics of the respondents in the Chancellery building are summarized in Table 2. The respondents are categorized by age, gender, number of occupants in the working area, sitting next to a window or not, years worked in this building, years worked in the present working area, and floor. One hundred and fifty-two respondents (76.0%) are 30 years old or over and one hundred and forty respondents (71.1%) are females. One hundred and eighteen respondents (59.0%) share their working area with more than eight occupants and one hundred and seventeen respondents (56.5%) do not sit next to a window. One hundred and seventy-nine respondents (86.5%) have been working in the case study building for a year or more, while one hundred and sixty-six respondents (80.2%) have been working in the same work area for a year or more.
The obtained results focused on measuring fifteen variables, divided into four main groups, as shown in Table 3. The first group covers temperature variables, namely temperature comfort (uncomfortable–comfortable), temperature range (too hot–too cold) and temperature stability (stable–varies during the day). In contrast, the second group contains air humidity and velocity variables that are air movement (still–draughty), air humidity (dry–humid), air freshness (fresh–stuffy), air smell (odorless–smelly), and air conditions overall (unsatisfactory–satisfactory). However, the third group consists of lighting variables, i.e., lighting overall satisfactory (unsatisfactory–satisfactory), natural light (too little–too much), glare from the sun and sky (none–too much), artificial light (too little–too much), and glare from lights (none–too much) were also measured.
Moreover, for the fourth group of variables, the respondents were asked to provide their general opinion about the overall comfort of their working conditions (unsatisfactory–satisfactory) and productivity at work (−40% or less to +40% or more) as essential variables. The benchmarking results indicated how well or poorly the conditions in the Chancellery Building the results compared with other buildings benchmarked by the BUS Methodology. For instance, the temperature is not suitable as it is colder than the benchmark (shown in red).
The number of valid responses for this test was 169. The minimum sample size required is 69 for a significant hierarchical multiple regression test within two sets of variables: set A with six variables and set B with thirteen variables [45]. Accordingly, the influence of temperature, humidity, air velocity, and illuminance were analyzed on overall comfort. Control variables were considered in the analysis to ensure that overall comfort was not affected by other variables other than building conditions. The following variables were considered as control variables for comfort, namely, age (less than 30 or more than 30), gender (male or female), and the number of occupants of the office (working alone, shared with 1 person, shared with 2–4 others, shared with 5–8 others, or shared with more than 8 others). Other variables were sitting next to a window (yes or no), the duration of working in this building (less than a year or more than a year), and how long you have worked in your present work area (less than a year or more than a year). The results are presented for comfort as follows.
First, the results showed that the first block of control variables had no significant influence on comfort, as shown in Table 4. The building conditions significantly influenced comfort when control variables were considered (R2 Change = 0.444). The result shows that building conditions have about 42% influence on staff level of comfort (Adjusted R2= 0.418).
Variables that have great influence on comfort were temperature range (Beta = 0.230), air conditions overall (Beta = 0.380), natural light (Beta = 0.227), glare from sun and sky (Beta = −0.224), artificial light (Beta = 0.180), and glare from lights (Beta = −0.197) as shown in the coefficient results (Table 5).
The number of valid responses for this test was 163. The minimum sample size required is 72 for a significant hierarchical multiple regression test within two sets of variables, set (A) six variables and set (B) fourteen variables [45]. Accordingly, productivity was analyzed on the influence of temperature, humidity, air velocity, light, and overall comfort. Control variables were considered in the analysis to ensure that productivity was not affected by other variables other than building conditions. The following variables were considered as control variables for productivity, namely, age (less than 30 or more than 30), gender (male or female), and the number of occupants of the office (working alone, shared with 1 person, shared with 2–4 others, shared with 5–8 others, or shared with more than 8 others). Other variables are, sitting next to a window (yes or no), the duration of working in this building (less than a year or more than a year), and how long you have worked in your present work area (less than a year or more than a year). The results for productivity are presented as follows.
First, the results show that the first block of control variables has no significant influence on productivity, as shown in Table 6. Building conditions and comfort significantly influence productivity when control variables are considered (R2 Change = 0.160). The overall results indicated that building conditions have about 8% influence on staff productivity (Adjusted R2 = 0.075).
Table 7 demonstrates the variable that significantly influences productivity, the overall comfort is (Beta = 0.305) as shown in the coefficient results. Age has an almost significant influence on productivity but with small Beta value.
The critical indoor comfort factors that influence occupants’ comfort and productivity have been identified to meet the first objective of the study with both benchmarking and SPSS hierarchical multiple regression as follows:
Benchmarking results indicated how well or poorly the conditions in the Chancellery Building compared with other buildings benchmarked by the BUS Methodology. Benchmarking for the whole building generally indicated that most temperature variables are shown in red, which means they are worse than the benchmark. In comparison, most air variables are amber, meaning there is no difference from the benchmark. However, most lighting variables are red, which shows that the lighting in this case study is worse than the benchmark. Moreover, the overall comfort variable is amber, which states that comfort in this building is similar to the benchmark; furthermore, the productivity variable is green, indicating that staff productivity for the chancellery building is better than the benchmark.
The hierarchical multiple regression test indicated that six variables of building conditions, namely, temperature range, air conditions overall, natural light, glare from sun and sky, artificial light, and glare from lights have a significant influence on staff comfort. Changes to these variables can have a change of about 42% to staff comfort which is quite high. As staff productivity is mainly influenced by the overall comfort of the building environment, an increase in overall comfort conditions can increase staff productivity by about 8%.
The physical conditions of the indoor environments are presented in Table 8. This part investigated the second objective in terms of understanding the physical factors of the indoor environmental conditions that affected the operations of the case study. The results demonstrated average findings of air temperature, relative humidity, air velocity, and illuminance during work days. By examining the collected data against the criteria of comfort conditioning in ASHRAE [46], it was found that more than 87.50% of data loggers did not meet comfort perception which should be adjusted to deliver comfortable and productive environments for the staff in the case study. Figure 5 illustrates the locations of data loggers that provide comfort perception. The green shades demonstrate the locations with comfortable conditions while the red shades indicate the locations that need to be improved.
Furthermore, the third method using simulation modelling was used to investigate the physical factors of the indoor environments. As shown in Figure 6, physical measurements and IES-VE results are similar, indicating the simulation results’ validity. The results showed that indoor comfort conditions are unstable during weekdays and even weekends. In short, the outdoor environmental condition quickly affects the Chancellery building envelope. The outdoor climate data used for simulation is the IES-VE Kuala Lumpur climate data file.
As shown in Figure 7, the simulation results indicated a similar pattern in relative humidity readings. These results pointed to a problem with cooling systems that deliver high humidity levels and add more loads to indoor environmental conditions. In addition, the simulation results showed that the illuminance measurement was similar to the data loggers, as shown in Figure 8.
Air conditions and temperature range are the most significant variables resulting in the multiple regression analysis; therefore, the simulation case scenarios have been based on changing the chillers’ temperature and working hours. The simulation was categorized into six main groups according to the chillers’ temperature case scenarios, namely, chillers set to 24 °C, 24.5 °C, 23.5 °C, 25 °C, 23 °C, and 25.5 °C. Each group is divided into 16 simulations. As a result, the total number of simulations was 96 case scenarios, including the chillers load outcomes calculated in MWh. All floors in the simulation analysis were adjusted into open plan offices as occupants’ perception and physical measurements in open plan floors demonstrated improved results.
The current yearly energy consumption for the chancellery building is 2,166,000 kWh, which means its current Building Energy Index (BEI) is 114.00 kWh/m2/year. Eighty-six out of ninety-six cases have less yearly energy consumption than the current situation. It is estimated that the reduction of 3 h in the air-conditioning operating hours resulted in 314,630 kWh of yearly energy consumption reduction. Thus, adjusting the working hours of the chillers from 0900 to 1600 with the same temperature, which is 24 °C, improves indoor thermal conditions inside the building and reduces the building’s BEI to 97.44 kWh/m2/year with an energy saving of 14.53%. Furthermore, it is possible to obtain the optimum results by reducing the air-conditioning operating hours by 3 h while increasing the temperature of the chillers by just 1.5 °C. Hence, adjusting the working hours of the chillers to 0900–1600 with a temperature of 25.5 °C maintained comfort inside the building. This adjustment also reduced the building’s BEI to 89.48 kWh/m2/year with an energy saving of 21.51%, as shown in Table 9.

4. Conclusions

By applying the developed triangulation method using Building Use Studies (BUS) for evaluating occupant satisfaction, physical measurements, and simulation modelling on the case study, energy consumption was reduced, and indoor environmental conditions and perceived productivity of staff were improved. This triangulation method provided a simple yet practical way to develop a greater understanding of building conditions and occupants’ requirements. The method, through the simulation trials, enabled the development of recommendations to reduce energy and enhance indoor conditions, comfort, and productivity. The main findings from this research are summarized below:
  • The general benchmarking results for the whole building indicate that most temperature variables are lower than the benchmark. However, most of the air variables have no difference from the benchmark. Moreover, the overall comfort variable was similar to the benchmark. Interestingly, the productivity variable was better than the benchmark.
  • The results of physical measurements indicated that 87.50% of the studied areas/zones do not comply with comfort perception. Thus, the majority of office areas should be adjusted to provide comfortable and productive environments.
  • The simulation analysis showed that a reduction of 3 h in the operating hours with an increase in chillers’ temperature by just 1.5 °C managed to provide optimum results. The application was demonstrated by adjusting the working hours of the chillers to 0900–1600 with a temperature of 25.5 °C, maintaining indoor conditions and reducing the building’s BEI to 89.48 kWh/m2/year with an energy saving of 21.51%.
  • This simple adjustment of chillers operation not only reduces the building’s BEI, but also can enhance staff comfort and productivity. The result of the hierarchical multiple regression analysis showed the expected level of change based on Beta values (i.e., considering all other variables can lead to a 42% change to staff comfort and 8% change to productivity).
Finally, this study offers a new strategy in providing a method for lowering carbon emissions and energy consumption while improving the productivity of users in existing office buildings.

Author Contributions

Conceptualization, M.H.A.-S., M.A.I., A.M.A. and K.M.A.-O.; Formal analysis, M.H.A.-S.; Investigation, M.H.A.-S.; Methodology, A.M.A.; Supervision, M.A.I. and A.M.A.; Validation, M.A.I. and K.M.A.-O.; Writing—original draft, M.H.A.-S.; Writing—review & editing, M.A.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by University of Malaya: LL017-16SUS.

Acknowledgments

The authors thank top management and staff of UM Chancellery and JPPHB for their collaboration during data collection. In addition, the authors thank Adrian Leaman for providing the license to use the BUS survey.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Theoretical Framework.
Figure 1. Theoretical Framework.
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Figure 2. Developed triangulation method to assess indoor environmental conditions and staff productivity.
Figure 2. Developed triangulation method to assess indoor environmental conditions and staff productivity.
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Figure 3. Universiti Malaya Chancellery Building.
Figure 3. Universiti Malaya Chancellery Building.
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Figure 4. Typical floor (9th) of UM Chancellery Building and distribution of data loggers. There are two air-conditioned offices on this floor in light blue and pink. HOBO data loggers were located at H1–H8 (close to the curtain walls and deep inside the offices). AHU is the air-handling unit.
Figure 4. Typical floor (9th) of UM Chancellery Building and distribution of data loggers. There are two air-conditioned offices on this floor in light blue and pink. HOBO data loggers were located at H1–H8 (close to the curtain walls and deep inside the offices). AHU is the air-handling unit.
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Figure 5. Locations of data loggers and perceived comfort in different floors. HOBO data loggers were located at H1–H8 (close to the curtain walls and deep inside the offices). AHU is the air-handling unit.
Figure 5. Locations of data loggers and perceived comfort in different floors. HOBO data loggers were located at H1–H8 (close to the curtain walls and deep inside the offices). AHU is the air-handling unit.
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Figure 6. Indoor air temperature for 7 days in level 6 (field measurements on the left and simulation results on the right) [18].
Figure 6. Indoor air temperature for 7 days in level 6 (field measurements on the left and simulation results on the right) [18].
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Figure 7. Readings of relative humidity for 7 days in level 6 (field measurements on the left and simulation results on the right) [18].
Figure 7. Readings of relative humidity for 7 days in level 6 (field measurements on the left and simulation results on the right) [18].
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Figure 8. Readings of illuminance levels for 7 days in level 6 (field measurements on the left and simulation results on the right) [18].
Figure 8. Readings of illuminance levels for 7 days in level 6 (field measurements on the left and simulation results on the right) [18].
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Table 1. Summary of Research Methodology.
Table 1. Summary of Research Methodology.
Research
Question
Research
Objective
Method
Approached
Purpose of MethodType of
Analysis
Q.1. What essential indoor comfort factors influence occupants’ satisfaction and productivity?1st Objective: To identify the critical indoor comfort factors influencing
occupants’ satisfaction and productivity.
Developed triangulation methodology to measure a case study performanceStructured questionnaire survey method (BUS survey)To indicate the occupants’ perception of the indoor comfort conditions and satisfactionBUS benchmarking, SPSS hierarchical multiple regression
Q.2. How do indoor environmental conditions affect the operations of an office building in Malaysia?2nd Objective: To evaluate the effects of indoor environmental conditions at an office building in Malaysia.Physical measurements method (HOBOs data loggers)To acquire the indoor conditions during the same period of the occupants’ perceptionHOBOware interpretation, descriptive analysis
Q.3. How to reduce the Building Energy Index (BEI) of an office building in Malaysia without interfering with the comfort and productivity of the staff?3rd Objective: To develop a method for reducing the Building Energy Index (BEI) for office buildings in Malaysia with comfortable and productive staff.Simulation modelling method (IES-VE software)To obtain the lowest BEI results without interfering with the staff comfort and productivitySimulate several case scenarios with indoor environment enhancements
Table 2. Demographic characteristics of the respondents (all floors).
Table 2. Demographic characteristics of the respondents (all floors).
DemographicCharacteristicsMissingFrequencyValid Percentage
AgeUnder 30 years84824.0%
30 or over15276.0%
GenderMale115728.9%
Female14071.1%
Number of occupants in the working areaAlone82412.0%
Shared with 1 other84.0%
With 2–4 others2211.0%
With 5–8 others2814.0%
With more than 811859.0%
Setting next to a windowYes19043.5%
No11756.5%
Worked in this buildingLess than a year12813.5%
A year or more17986.5%
Worked in present work areaLess than a year14119.8%
A year or more16680.2%
FloorGround floor (OS)-2311.1%
Second floor (OS)3315.9%
Third floor (CS)2311.1%
Fourth floor41.9%
Fifth floor10.5%
Sixth floor (OS)4119.7%
Seventh floor (CS)3516.8%
Eighth floor (CS)2311.1%
Ninth floor (CS)2512.0%
Table 3. The BUS survey results for all building floors.
Table 3. The BUS survey results for all building floors.
VariableMeanStd.
Error of Mean
Std.
Deviation
VarianceBenchmarking Results
Temperature Variables
Temperature: comfort overall4.740.0761.0731.152Green (above the benchmark—comfortable)
Range: hot/cold4.840.0791.1111.235Red (above the benchmark—too cold)
Stability: stable/varies4.510.0781.1071.226Red (above the benchmark—varies)
Air (Humidity & Velocity) Variables
Air movement: still/draughty4.050.0791.1091.230Green (above the benchmark—acceptable)
Air humidity: dry/humid4.120.0680.9530.908Amber (no difference with the benchmark)
Air freshness: fresh/stuffy4.110.0741.0461.094Amber (no difference with the benchmark)
Air smell: odorless/smelly3.910.0801.1261.267Amber (no difference with the benchmark)
Air Conditions overall4.760.0620.8860.784Green (above the benchmark—satisfactory)
Lighting Variables
Lighting: overall satisfactory4.790.0771.1001.210Amber (no difference with the benchmark)
Natural light: too little/too much4.360.0861.2251.501Red (above the benchmark—too much)
Glare from the sun:
none/too much
3.980.1051.4902.220Amber (no difference with the benchmark)
Artificial light: too little/too much4.370.0791.1151.244Red (above the benchmark—too much)
Glare from lights: none/too much4.210.0841.1951.429Red (above the benchmark—too much)
Other Essential Variables
Comfort: overall satisfactory4.870.0580.8290.687Amber (no difference with the benchmark)
Productivity:
−40% to +40%
6.55
+15.5%
0.1001.3881.925Green (above the benchmark—increased)
Table 4. Influence of temperature, air, and light on comfort (Model Summary).
Table 4. Influence of temperature, air, and light on comfort (Model Summary).
ModelRR2Adjusted R2Std. Error of the EstimateChange StatisticsDurbin-Watson
R2 ChangeF Changedf1df2Sig. F Change
10.200 a0.0400.0040.7930.0401.12561620.350
20.695 b0.4840.4180.6060.4449.842131490.0001.963
a. Predictors: (Constant), How long have you worked in your present work area? Do you sit next to a window in your regular workspace? Is your office or work area? What is your sex? What is your age? How long have you worked in this building? b. Predictors: (Constant), How long have you worked in your present work area? Do you sit next to a window in your regular workspace? Is your office or work area? What is your sex? What is your age? How long have you worked in this building? Air smell, Temperature stable, Comfort conditions overall, Glare from lights, Air humidity, Natural light, Temperature (hot–cold), Temperature comfort, Lighting overall, Air movement, Artificial light, Glare from sun and sky, Air freshness.
Table 5. Coefficient results of the influence of building conditions on comfort.
Table 5. Coefficient results of the influence of building conditions on comfort.
ModelUnstandardized
Coefficients
Standardized CoefficientsSig.Collinearity
Statistics
BStd. ErrorBetaToleranceVIF
1(Constant)4.8800.542 0.000
What is your age?0.2390.1530.1260.1200.9111.098
What is your sex?0.1450.1350.0850.2840.9551.047
Is your office or work area?−0.0120.045−0.0200.7970.9721.029
Do you sit next to a window in your regular workspace?0.0380.1240.0240.7580.9881.012
How long have you worked in this building?−0.3120.236−0.1300.1870.6161.624
How long have you worked in your present work area?−0.0730.193−0.0370.7040.6371.571
2(Constant)1.9880.635 0.002
What is your age?0.0720.1240.0380.5600.8111.234
What is your sex?0.2210.1090.1290.0450.8551.169
Is your office or work area?−0.0120.036−0.0200.7450.9151.093
Do you sit next to a window in your regular workspace?−0.0830.106−0.0510.4390.7871.271
How long have you worked in this building?−0.2690.185−0.1120.1480.5821.717
How long have you worked in your present work area?−0.0290.154−0.0150.8490.5851.709
Temperature comfort0.0460.0580.0610.4310.5901.696
Temperature (hot-cold)0.1610.0530.2300.0030.6051.653
Temperature stable−0.0100.056−0.0130.8610.5961.677
Air movement−0.0580.058−0.0810.3230.5171.934
Air humidity0.0390.0780.0450.6160.4402.272
Air freshness0.0510.0770.0630.5060.3832.613
Air smell−0.0930.058−0.1240.1080.5911.691
Air conditions overall (Air)0.3520.0670.3800.0000.6651.503
Lighting overall0.1030.0610.1380.0950.5171.935
Natural light0.1560.0620.2270.0130.4242.359
Glare from the sun and sky−0.1190.049−0.2240.0160.4112.435
Artificial light0.1300.0600.1800.0320.4982.008
Glare from lights−0.1360.054−0.1970.0120.5711.752
Table 6. Influence of temperature, air, lighting, and comfort on productivity (Model Summary).
Table 6. Influence of temperature, air, lighting, and comfort on productivity (Model Summary).
ModelRR2Adjusted R2Std. Error of the EstimateChange StatisticsDurbin-Watson
R2 ChangeF Changedf1df2Sig. F Change
10.173 a0.030−0.0071.3840.0300.80561560.567
20.435 b0.1900.0751.3260.1601.998141420.0222.170
a. Predictors: (Constant), How long have you worked in your present work area? Is your office or work area? Do you sit next to a window in your regular workspace? What is your sex? What is your age? How long have you worked in this building? b. Predictors: (Constant), How long have you worked in your present work area? Is your office or work area? Do you sit next to a window in your regular workspace? What is your sex? What is your age? How long have you worked in this building? Air smell, Temperature stable, Comfort conditions overall, Glare from lights, Air humidity, Natural light, Temperature (hot–cold), Temperature comfort, and Lighting overall. All things considered, how do you rate the overall comfort of the building environment? Air movement, Artificial light, Glare from sun and sky, Air freshness.
Table 7. Coefficient results of the influence of building conditions and comfort on productivity.
Table 7. Coefficient results of the influence of building conditions and comfort on productivity.
ModelUnstandardized
Coefficients
Standardized CoefficientsSig.Collinearity
Statistics
BStd. ErrorBetaToleranceVIF
1(Constant)6.5100.950 0.000
What is your age?0.5300.2730.1610.0540.9021.109
What is your sex?0.0290.2390.0100.9020.9561.046
Is your office or work area?0.0030.0810.0030.9750.9731.028
Do you sit next to a window in your regular workspace?0.0450.2210.0160.8380.9821.019
How long have you worked in this building?−0.3010.428−0.0720.4820.5971.674
How long have you worked in your present work area?−0.2150.352−0.0610.5430.6301.587
2(Constant)4.8191.468 0.001
What is your age?0.2360.2780.0720.3990.7931.261
What is your sex?0.1210.2470.0410.6240.8211.217
Is your office or work area?0.0130.0810.0130.8750.8971.115
Do you sit next to a window in your regular workspace?0.0760.2420.0270.7560.7521.330
How long have you worked in this building?−0.2620.425−0.0620.5390.5551.801
How long have you worked in your present work area?−0.0500.355−0.0140.8890.5701.756
Temperature comfort−0.0180.131−0.0140.8930.5681.761
Temperature (hot-cold)−0.0870.120−0.0720.4720.5661.767
Temperature stable−0.0610.129−0.0480.6380.5551.802
Air movement−0.0630.130−0.0520.6250.5041.984
Air humidity−0.0020.173−0.0010.9900.4412.265
Air freshness0.1220.1710.0870.4770.3802.628
Air smell−0.1470.129−0.1130.2570.5751.740
Air conditions overall0.1900.1600.1190.2380.5671.763
Lighting overall−0.0660.137−0.0510.6310.5141.945
Natural light−0.0810.139−0.0690.5600.4032.484
Glare from the sun and sky0.0660.1100.0730.5500.3892.571
Artificial light0.1200.1340.0980.3720.4792.086
Glare from lights−0.2180.120−0.1850.0730.5461.833
All things considered, how do you rate the overall comfort of the building environment?0.5310.1830.3050.0040.5181.930
Table 8. Average readings of all physical measurements at all levels.
Table 8. Average readings of all physical measurements at all levels.
FloorData LoggerTemp °CRH %Vel. m/sIlluminance Lux
9:0012:0015:00
Level 2H121.6973.360.170157.68212.86208.93
H221.8865.440.055108.40143.90112.35
H322.7074.101.152100.48122.20124.18
H425.4153.931.0702524.754964.784229.65
H521.6965.500.806404.03703.63918.48
H620.8768.900.385106.40137.98151.75
H725.5762.100.386806.101395.431198.33
H823.6869.450.950236.50376.45411.93
Level 6H124.2963.940.068985.481326.45812.03
H222.6775.740.165114.30124.18130.08
H322.4172.171.186171.48203.00191.20
H421.9773.290.54992.6590.7092.65
H525.1865.030.759788.381022.93618.90
H623.8165.720.302120.23132.05102.48
H722.2277.290.618214.83197.08120.23
H823.7669.941.0381046.551722.601409.23
Level 7H123.2372.190.0551320.553244.182717.90
H224.0373.440.074323.25406.03346.90
H324.2572.461.214218.775281.83201.05
H422.8775.240.54986.73122.18114.30
H523.3871.920.956934.202244.901923.63
H623.6170.580.372216.78319.30275.93
H722.8777.170.600139.95151.73147.76
H824.3868.311.1221241.702438.081509.75
Level 8H122.9275.390.0891322.482497.231643.75
H223.2573.880.053183.30230.60155.70
H323.3371.921.220386.33354.76193.15
H422.3777.820.683250.33425.73266.08
H523.5171.720.8631805.381501.85354.78
H623.1574.300.38069.0080.8361.10
H721.8880.210.782143.85157.65139.93
H821.8280.320.79151.2051.2027.55
Table 9. Simulation scenarios with various chiller temperatures to achieve energy efficiency.
Table 9. Simulation scenarios with various chiller temperatures to achieve energy efficiency.
No.Case Scenarios SimulationYearly Energy
Consumption (kWh)
BEI (kWh/m2/year)Energy
Saving
ON-OFF Temp (°C)
10900–160025.51,700,19089.4821.51%
20830–160025.51,741,86091.6819.58%
30900–160025.01,750,74092.1419.18%
40900–163025.51,773,41093.3418.12%
50800–160025.51,774,74093.4118.06%
60830–160025.01,794,43094.4417.16%
70730–160025.51,798,94094.6816.95%
80900–160024.51,801,13094.8016.84%
90830–163025.51,812,59095.4016.32%
100900–163025.01,826,03096.1115.69%
110800–160025.01,829,25096.2815.54%
120900–170025.51,841,64096.9314.97%
130800–163025.51,843,31097.0214.89%
140830–160024.51,846,85097.2014.74%
150900–160024.01,851,37097.4414.53%
160730–160025.01,855,30097.6514.34%
170730–163025.51,865,69098.1913.87%
180830–163025.01,867,15098.2713.80%
190900–163024.51,878,50098.8613.28%
200830–170025.51,878,56098.8713.27%
210800–160024.51,883,60099.1413.04%
220900–173025.51,893,11099.6412.60%
230900–170025.01,896,26099.8012.46%
240830–160024.01,899,10099.9512.32%
250800–163025.01,899,74099.9812.30%
260900–160023.51,901,440100.0812.21%
270800–170025.51,907,320100.3911.94%
280730–160024.51,911,500100.6111.75%
290830–163024.51,921,560101.1311.29%
300730–163025.01,923,880101.2611.18%
310730–170025.51,928,020101.4710.99%
320830–173025.51,928,190101.4810.98%
330900–163024.01,930,800101.6210.86%
340830–170025.01,935,040101.8410.67%
350800–160024.01,937,790102.0010.53%
360900–173025.01,949,650102.6110.00%
370900–170024.51,950,720102.679.94%
380830–160023.51,951,200102.699.92%
390900–160023.01,951,360102.709.91%
400800–173025.51,955,380102.919.73%
410800–163024.51,956,000102.959.69%
420800–170025.01,965,580103.459.25%
430730–160024.01,967,540103.559.17%
440730–173025.51,974,760103.938.83%
450830–163024.01,975,800104.008.77%
460730–163024.51,981,910104.318.50%
470900–163023.51,982,950104.378.44%
480830–173025.01,986,510104.558.29%
490730–170025.01,987,980104.638.22%
500830–170024.51,991,350104.818.06%
510800–160023.51,991,820104.838.04%
520830–160023.02,003,130105.437.52%
530900–170024.02,005,010105.537.43%
540900–173024.52,006,030105.587.39%
550800–163024.02,012,090105.907.11%
560800–173025.02,015,410106.076.96%
570730–160023.52,023,410106.506.58%
580800–170024.52,023,680106.516.57%
590830–163023.52,029,870106.846.28%
600900–163023.02,034,930107.106.05%
610730–173025.02,036,420107.185.98%
620730–163024.02,039,780107.365.82%
630830–173024.52,044,670107.615.61%
640800–160023.02,045,680107.675.55%
650830–170024.02,047,500107.765.47%
660730–170024.52,047,780107.785.46%
670900–170023.52,059,140108.384.93%
680900–173024.02,062,250108.544.79%
690800–163023.52,068,020108.844.53%
700800–173024.52,075,280109.234.18%
710730–160023.02,079,110109.434.01%
720800–170024.02,081,600109.563.89%
730830–163023.02,083,790109.673.80%
740730–163023.52,097,470110.393.17%
750730–173024.52,097,920110.423.14%
760830–173024.02,102,660110.672.92%
770830–170023.52,103,480110.712.89%
780730–170024.02,107,400110.922.70%
790900–170023.02,113,110111.222.44%
800900–173023.52,118,290111.492.20%
810800–163023.02,123,780111.781.95%
820800–173024.02,134,980112.371.43%
830800–170023.52,139,360112.601.23%
840730–163023.02,155,000113.420.51%
850730–173024.02,159,250113.640.32%
860830–170023.02,159,300113.650.31%
87Existing2,166,000114.000.00%
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Al-Sabahi, M.H.; Ismail, M.A.; Alashwal, A.M.; Al-Obaidi, K.M. Triangulation Method to Assess Indoor Environmental Conditions and Occupant Comfort and Productivity towards Low Energy Buildings in Malaysia. Buildings 2022, 12, 1788. https://doi.org/10.3390/buildings12111788

AMA Style

Al-Sabahi MH, Ismail MA, Alashwal AM, Al-Obaidi KM. Triangulation Method to Assess Indoor Environmental Conditions and Occupant Comfort and Productivity towards Low Energy Buildings in Malaysia. Buildings. 2022; 12(11):1788. https://doi.org/10.3390/buildings12111788

Chicago/Turabian Style

Al-Sabahi, Mohammed Hatim, Muhammad Azzam Ismail, Ali Mohammed Alashwal, and Karam M. Al-Obaidi. 2022. "Triangulation Method to Assess Indoor Environmental Conditions and Occupant Comfort and Productivity towards Low Energy Buildings in Malaysia" Buildings 12, no. 11: 1788. https://doi.org/10.3390/buildings12111788

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