Energy consumption to cool an indoor environment is a substantial part of total energy end-use, particularly in a tropical climate with high energy demand for cooling. To improve energy efficiency, cooling systems can be optimized using a variety of neutral indoor temperatures to maintain a balance between an occupant’s thermal comfort and cooling energy demand. This explanatory study investigated the thermal quality and cooling energy demand of a Platinum-certified office building in the tropical climate of Malaysia. The investigation aimed to suggest a balance between occupant thermal comfort and cooling energy demand. The thermal investigation includes an objective field measurement that implements environmental equipment to monitor thermal quality and a subjective occupant’s thermal feedback using a questionnaire survey. To calculate cooling energy demand, the total equivalent temperature difference method (TETD) is applied. The results suggested an occupant’s cooling sensation of around 24 °C, with no significant difference concerning age and gender. Cooling load calculation indicated a 36% energy reduction by increasing air temperature to 26 °C, for occupants to feel thermally comfortable in a tropical climate. These findings contribute to improving sustainable energy policies, sustainable construction, and thermal comfort improvement for a tropical climate.

The quality of the indoor environment has become a vital component for buildings due to the time spent indoors. To this extent, the performance of the indoor environment is considered as part of the greenery criteria by green rating schemes such as the Green Building Index in Malaysia. This study aims to investigate and assess the quality of the indoor environment of Platinum-certified office buildings in a tropical climate. This research applied a case study approach over two Platinum-certified office buildings. Post-occupancy evaluation is employed integrating full-scale measurement with an occupants’ survey. The measurement was carried out from May to August, and 112 questionnaires were retrieved to evaluate occupants’ satisfaction with aspects of the indoor environment. Thermal comfort, indoor air quality, acoustic, lighting, furniture, and cleanliness are considered as the main study variables. The findings of full-scale measurement indicated high relative humidity, and low air velocity and illuminance. While occupants reported overall indoor environment quality (IEQ) comfort, a significant correlation of variables was observed. The main sources of dissatisfaction were identified as overcooling around 24 °C, high relative humidity (RH), around 70% RH, glare, and background noise around 51.9 dB. Statistically, a significant difference between occupants’ responses to IEQ of two cases was identified, although both buildings are labelled with a Platinum certificate.

Natural ventilation has been used widely in buildings to deliver a healthy and comfortable indoor environment for occupants. It also reduces the consumption of energy in the built environment and dilutes the concentration of carbon dioxide. Various methods and techniques have been used to evaluate and predict indoor airspeed and patterns in buildings. However, few studies have been implemented to investigate the relevant methods and tools for the evaluation of ventilation performance in indoor and outdoor spaces. The current study aims to review available methods, identifying reliable ones to apply in future research. This study investigates scientific databases and compares the advantages and drawbacks of methods including analytical models, empirical models, zonal models, and CFD models. The findings indicated the computational fluid dynamics (CFD) model is the most relevant method because of cost-effectiveness, informative technique, and proficiency to predict air velocity patterns and ratios in buildings. Finally, widely used CFD codes and tools are compared considering previous studies. It is concluded the application of codes for research is subject to the complexity and characteristics of a studied model, the area and field of study, the desired turbulence model, and the user interface.

Applying any sustainable intervention in the urban energy system requires fundamental knowledge of the energy demand dynamics. Only when we can predict the users' energy demand at any given time with accuracy, we can redesign the urban energy system. Accordingly, the main objective of this paper is to determine the annual electricity usage of the building connections in the urban built environment. In this paper firstly through a literature review, the important electricity usage explanatory variables of the built environment are recognized. For each building, besides the annual electricity usage, three major categories of explanatory variables, including physical, socioeconomic, and geospatial characteristics are determined. Based on the available data sources, a building electricity usage database is created. The database is categorized based on the two most frequently used building sectors including residential and non-residential. Ordinary Least Squares (OLS) technique is applied to the constructed database to estimate the predicting model parameters establishing a relationship between the annual electricity usage as a dependent variable and physical, socioeconomic, and geospatial variables as independent variables. In this research, to determine the contribution of geospatial characteristics in the annual electricity usage variability, regression analysis is performed in two consecutive steps. In the first step only, the geospatial characteristics were implemented in the multiple linear regression analysis. Following that, in the second step, the other categories including physical and socioeconomic characteristics are added to the model. The result revealed that in both building sectors most of the predictors are statistically significant at the 0.05 level. While for the residential buildings the geospatial characteristics account for 9.7% of the electricity usage variation, these values for the service and industry sub-sectors are 9.9% and 8.7% respectively. In total, all variables explain 28.1%, 39.4%, and 42.9% of the electricity usage variability of residential, service, and industrial buildings respectively.