This study investigates the impact of an active workstation on heating and cooling energy consumption in commercial buildings. The improved performance and health of office workers are well-known and supported by several surveys and studies. However, the preferred and revised indoor air temperature for different activities of the employees, such as standing and walking, directly impacts the energy consumption of the building. Therefore, this study systematically quantifies the impact of indoor environments on building energy consumption using a simulation tool. Based on literature reviews with multidisciplinary fields, revised setpoint temperature, metabolic rate, and required outdoor air rate due to the level of active workstation (standing and walking) are applied to EnergyPlus reference building models. The impact on heating and cooling energy consumption is analyzed by considering several representative cities in the United States. Their average sensitivity was found to be 7.28% lower and 14.28% higher for heating and cooling energy, respectively. The total increment of $13,887 represents approximately 0.16% of the total profit of the corporation. This increment can be justified given the benefits to workers’ health, which include lower insurance costs and improved performance. This suggests that it is economically desirable to deploy active workstations widely.

This study proposes a methodology for fairly assessing the building energy usage level of occupants using a public open dataset. A case study of day-care centers in South Korea was conducted to demonstrate the methodology. An open dataset of monthly building energy consumption in the day-care centers was obtained and grouped based on thermal performance (e.g., U-value). For each performance group, monthly electricity consumption (representing cooling demand), gas consumption (representing heating demand), and energy consumption were segmented using k-means clustering into heavy, medium, and light users. For each user cluster, representative monthly trajectories were ascertained by averaging the values. Using the input variables of the building performance and environmental factors, the machine learning-based evaluation models were developed to purely infer the impact of the occupants on energy consumption (monthly trajectories). All models exhibited reasonable performance (12% cv(RMSE) in the worst case); the linear regression model is recommended for its simplicity and applicability in policymaking and decision-making contexts. Finally, the efficacy of the developed model in evaluating energy usage levels is presented with an example.

The reference building models in the EnergyPlus tool do not consider the stack effect. This is not an issue for small and low-rise buildings. However, this is significant in evaluating the heating and cooling load of high-rise buildings. This study evaluates and quantifies this stack effect in building energy simulation, EnergyPlus. The high-rise commercial building model in the EnergyPlus reference building set was used as a simulation test bed. The vertical multi-stories shaft zones were added inside the core zone in the existing building model. Then, the airflow network model was set in EnergyPlus so the internal airflow including the vertical and horizontal directions through the shaft zone as well as the core and perimeter zones were configured. The simulation model was calibrated against the actual pressure distribution between the core and perimeter zones in all floors of high-rise buildings from the literature. This model calibration was conducted manually by changing the discharge coefficient of the openings between thermal zones. The heating and cooling load of the proposed model were compared to the existing model which does not consider the vertical airflow through the shaft zones. The quantification with relative load mismatch was analyzed by floors and environment including the temperature difference between indoor and outdoor, and wind speed. Moreover, the impact of the air mixing and infiltration on the heating and cooling load was quantified. The result of this study reveals the significance of the stack effect in calculating the heating and cooling load.