Modeling of thermal energy saving in commercial buildings of Australia by balanced tree growth optimizer

Abstract

Low-energy cases can be designed more efficiently by using analytical optimization. The non-linear thermal performance of buildings has led to the development of optimization techniques based on simulation. In building optimization, it is important to achieve superior solutions while minimizing calculation expenses. This study aims to optimize an Australian office building using the Balanced Tree Growth Optimizer (BTGO). It has resulted that more than 11.7 % of energy can be saved by the optimization process and also some energy-saving measures. A comparison of the utilized algorithm with benchmark algorithms including the Nelder-Mead method, hybrid Particle Swarm Optimization, Hooke-Jeeves, and Ant Colony Optimization for continuous domain showed that the BTGO can achieve better solutions and needs less computational time.