Multi-objective Optimization of Envelope Retrofits for Improved Energy Performance in an Educational Building

With the advent of global warming, coupled with the building sector accounting for 40% of total annual energy consumption and a leading contributor to carbon emissions, energy efficiency in buildings is essential. While upcoming building stock can facilitate the energy-saving approaches starting with the design stage, retrofitting remains the sole alternative for the existing buildings. The current study investigates the impact of retrofitting design variables on energy consumption for an educational building in tropical Mumbai. The study employed primary data collection, model development, and generation of iterated scenarios followed by energy simulations and multi-objective optimization. While Python was used for generating iterations and optimizing scenarios, energy performance was done using Rhino/Grasshopper plugins LadyBug and HonyeBee and validated using electricity bills. The energy performance was investigated for 40 scenarios generated by varying wall and roof conductivity, thickness, solar absorptance, and shading angle. The findings show that increasing insulation thickness while decreasing solar absorptance can reduce energy consumption. The multi-objective optimization results in optimal scenarios with maximum energy savings and minimum cost incurred. This study paves the way for future energy-efficient built-environment design guidelines.