With rapid urbanization, massive amount of energy is required to compensate the electricity usage thus calls for a need to Malaysian government issuing standard MS1525:2014 for temperature settings in office buildings to meet energy efficiency goal. In co-sharing spaces, personal thermal comfort is often not met due to the different thermal sensation at different location inside office rooms. This study was conducted at four postgraduate office spaces with cooling mode in university campus located at Kuala Lumpur to evaluate the occupant’s thermal sensation. We used different set-point temperature of air conditioning ranging from 18.0°C to 28.6°C. The indoor thermal variables such as air temperature, globe temperature, relative humidity, and air velocity are measured at each respondent’s workspace and 200 responses were recorded from ten subjects. The mean value of thermal sensations votes is -0.4 and were within comfort range. 76% of responses voted ‘neutral’ humidity sensation as occupants have adapted to humid condition in Malaysia. The comfort operative temperature found in this study is 24.9°C which indicates that the minimum recommended temperature for energy conservation did not deprive occupants from comfort.
Personal comfort models were developed to circumvent most of the constraints imposed by the Predicted Mean Vote (PMV) and present adaptive models, which consider the average response of a large population. Although there has been a lot of research into new input features for personal comfort models, the spatial data of the building, such as windows, doors, furniture, walls, fans, and heating, ventilation, and air conditioning (HVAC) systems, (the location of its occupants with those elements), have not been thoroughly examined. This paper investigates the impact of the spatial parameter in predicting personal indoor thermal comfort using various machine learning approaches in air-conditioning offices under hot and humid climates. The Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, and Neural Network were trained using a field study dataset that was done in nineteen office spaces yielding 628 samples from 42 occupants. The dataset is divided randomly into training and testing datasets, with a ratio of 80% and 20%. This study examines how well machine learning predicts personal thermal comfort with spatial data compared to without spatial data; where the spatial parameters have shown a significant influence on model prediction accuracies, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The result shows the average MAE is decreased by 10.6% with the Random Forest (RF) getting the most MAE reduction by 23.8%. Meanwhile, the average RMSE is reduced by 11.8% with the RF giving the most RMSE cutback by 30.6%. Consequently, the spatial effect analysis also determines which area of the room has cold or heat clusters area that affects thermal comfort that contributes to the design of sustainable buildings.
Apart from indoor environmental and personal factors, contextual factors have significantly influenced several thermal comfort studies. In air-conditioned spaces, thermal comfort is conveniently attainable by adjusting the temperature settings, but indoor design elements might alter thermal perceptions and provide adaptive opportunities. This study examines the influence of office design characteristics and anthropometrics on thermal comfort parameters and perceptions. Nineteen university offices in Kuala Lumpur and Shah Alam, comprised of twelve shared and seven private spaces, were investigated, and 628 responses were collected from 42 participants with even gender distributions. The results showed that room occupancy and size were statistically significant with Griffiths’ comfort temperature. Offices with five or more people had lower mean comfort temperature (24.1 ℃) than private offices (25.0 ℃). The mean comfort temperature in offices larger than 80 m2 was 23.7 ℃ with warmer thermal preference, while offices smaller than 40 m2 were approximately one-degree Celsius higher. Offices with no shading device, window blinds opened, and tiled floorings had mean comfort temperatures higher than 25.0 ℃. The findings also indicated that offices with more than a 60% glazing ratio have a slightly higher mean comfort temperature at 24.9 ℃. The thermal sensation during closed blinds was much cooler than opened ones. The anthropometry of the human body impacts how heat is regulated; thus, respondents with higher Body Mass Index (BMI) and above-average body surface area (higher than 1.7 m2) had significantly lower comfort temperatures and preferred more humid surroundings. Mean comfort temperature was statistically significant with BMI with a noticeable difference between underweight (25.1 ℃), normal (24.5 ℃), and obese (23.9 ℃) BMIs. In this study, it is recommended that BMI be considered when positioning occupants in shared offices, and window blinds are an integral shading device for adjusting indoor thermal comfort levels.
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