Grey Wolf algorithmic framework for building energy optimization in India's Köppen-Geiger climatic zones

Abstract

ABSTRACT Grey Wolf Optimization (GWO) is an emerging evolutionary metaheuristic technique capable of solving challenging engineering problems. Despite its growing popularity, GWO's suitability for building design problems remains unexplored. This paper presents a novel algorithmic framework using EnergyPlus, EPLauncher and Matlab to implement a single and bi-objective GWO for building energy optimization. The goal is to identify optimal wall and window type, orientation, air conditioner's operational profiles and cooling setpoints consistent with minimum annual and peak cooling energy demands for a residential apartment building in five Köppen-Geiger climate zones across India. In place of testing the entire parametric space involving 5,76,000 possibilities, GWO identifies the optimal solutions inside 1250 trials (∼99% run reduction). The single and bi-objective GWO produces (83-97)% and (75-95)% annual and peak cooling demand reductions than a typical construction and operation scenario in the five climate zones. The optimized solutions recommend low thermal transmittance-high capacitance wall sections, 10–15% window-to-wall ratios and double glazed windows with a low solar gain coefficient. Further, optimal air conditioner operational parameters (setpoint and duration) are identified. The presented algorithmic framework is highly robust and can integrate can incorporate upcoming metaheuristic algorithms to perform single and multiobjective building energy optimizations.