Energy Optimization on Campus Building Using Sefaira

Abstract

Energy efficiency in buildings is an important target to be met to reduce the greenhouse effect’s impact. Simulation methods and techniques are essential for conducting energy analysis and producing targets more accurately and quickly. This research calculates and simulates the Energi Use Intensity (EUI) and Spatial Daylight Autonomy (sDA) in Semarang’s campus building. Simulations are carried out using Sefaira software as a plug-in to the Sketchup software. The first energy use simulation (EUI) analysis as a baseline was carried out using the ASHRAE 90.1-2019 material standard resulting in a 144 kWh/m2/yr. The second simulation on the facade’s conditions and existing materials becomes my baseline result increasing to EUI 157 kWh/m2/year. The first engineering improvement was to replace the existing TL/PL lighting with an LPD value of 15.0 watts/m2 and replaced it with LED with an efficiency of 5.0 watts/m2, which can reduce energy use 131 kWh/m2/year. Furthermore, the 2nd scenario reduced window openings on the west and east sides, which had an immense contribution to heat intake, resulting in an additional efficiency of 118 kWh/m2/year. In the third scenario, replacing all glass types with several alternatives results in a reduced value EUI 99-109 kWh/m2/year. Still, All changes to this type of glass resulted in a decrease in natural lighting quality so that the sDA value fell below 50%, with the status changing from my baseline “well lit” to “under lit.” 4th scenario, the analysis was carried out by replacing glass without reducing the wall openings (WWR reduction). The results show that all changes to glass types/colors resulted in a decrease in natural lighting quality, except for replacing the Panasap Euro Gray glass types that still provide 61% sDA and 11% ASE values, thus providing a “well lit” quality. Of the four scenarios, only scenarios 1 to 3 provide a value of natural lighting quality that remains “well lit.” Scenario 4 causes the rate to drop to “under-lit.” Furthermore, from scenarios 1, 2, and 3, the lowest EUI value is in scenario 2 with a value of 118 kWh/m2/yr, so the 2nd scenario is the best in this optimization process. The first section in your paper