Abstract
This research aims to introduce an optimized office workspace that meets both optimum daylight availability and thermal energy performance. To achieve this, the first step was developed through a parametric model using Grasshopper plug-in for Rhino. Afterwards, the daylight and energy plug-ins of Honeybee and Ladybug were employed to evaluate daylight and thermal energy performance in the second step and finally, the optimization method was applied using Octopus plug-in. The optimization processes were carried out under two scenarios to find the final optimized solutions. Before running the optimization, Sensitivity analysis was performed to comprehensively analyse the relationship between design variables and the three objective functions metrics of visual comfort and thermal energy performance. For the first scenario, single-objective optimization, and the second scenario is multi-objective optimization. The metrics of Useful Daylight Illuminance, Daylight Glare Probability, and Energy Use Intensity to evaluate the thermal energy performance, were compared to find the best optimum solutions. The last step was compared for each of these optimized solutions and selecting the final optimum solution. However, multi-objective optimization solutions of objective functions were the final optimal solutions nearest to the ideal. The final optimum solutions of the best design parameters of light-shelf can improve the total average of useful daylight illuminance by 62.50% 56.25% 57.50%, and 68.13% and thermal energy performance by +1.15% −4.62%, −6.81% and −3.05% in March, June, September, and March respectively, compared to the single-objective solutions of light-shelf parameters. While daylight glare probability slightly increased by +1.88% and +3.13% in March and December, respectively.
•This paper focuses on the optimal design of light-shelves.•Five design parameters (position height, external and internal angles, and external and internal depth ratios) are proposed.•Optimum parameter values are obtained by two scenarios of optimization; single-objective and multi-objective.•Using the final optimal solutions leads up to 68.13% improvement in daylight availability.•The total thermal energy performance declines by up to 6.81% using the optimal solutions.