Research Area: Metaheuristic Computing
In the present research, the Grey Wolf Optimizer (GWO) was used to minimize the yearly energy consumption of an office building in Seattle weather conditions. The GWO is a meta-heuristic optimization method, which was inspired by the hunting behavior of grey wolfs. The optimization method was coded and coupled with the EnergyPlus codes to perform the building optimization task. The impact of algorithm settings on the optimization performance of GWO was explored, and it was found that GWO could provide the best performance by using 40 wolfs. The optimized solutions of GWO were compared with other optimization algorithms in the literature, and it was found that the GWO could lead to an excellent optimum solution efficiently. One of the best optimization methods in the literature was Particle Swarm Optimization (PSO), which led to an optimum objective function of 133.5, while GWO resulted in the optimum value of 133. The multi-objective building optimization was also examined by GWO. The results showed that it could provide an excellent archive of non-dominant optimum solutions.
Keywords:
Building
energy consumption
Building optimization problems (BOPs)
Grey Wolf Optimizer (GWO)
EnergyPlus
Author(s) Name: Mehdi Ghalambaz, Reza Jalilzadeh Yengejeh, Amir Hossein Davami
Journal name: Case Studies in Thermal Engineering
Conferrence name:
Publisher name: ELSEVIER
DOI: 10.1016/j.csite.2021.101250
Volume Information: Case Studies in Thermal Engineering Volume 27, October 2021, 101250
