Use of Genetic Algorithm to Optimize Energy Efficiency, Construction Cost, and Daylight in Building Design

Abstract

Abstract Buildings are a major consumer of energy and electricity in the overall energy consumption profile of a city. According to the IPCC AR6 report, buildings contribute to 40% of the overall GHG emissions. Widespread transformations in system and performance are required to achieve the global target of 1.5 °C. Since the overall process of energy efficiency is based on several parameters and their associated cost functions, it is necessary to use suitable optimization techniques to find the most effective outcome focusing primarily on productivity, utilization, and efficiency. The study involves the application of a Genetic Algorithm for optimization techniques toward energy efficiency, construction cost, and daylight. A single-floor office building having a floor area of 1000 m2 has been simulated in EnergyPlus. Two optimization variables – Window-to-Wall Ratio and Glass SHGC have been considered for the study keeping the rest of the variables constant. The associated cost functions were the First Cost of the Building, Annual Operational Energy, and the Daylight Area. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) was applied for the study. The results were compared with the simulation values and optimal solution convergence was observed.