Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II)

Multi-objective optimization methods provide a valid support to buildings' design. They aim at identifying the most promising building variants on the basis of diverse and potentially contrasting needs. However, optimization has been mainly used to optimize the energy performance of buildings, giving secondary importance to thermal comfort and usually neglecting visual comfort and the indoor air quality. The present study addresses the design of a detached net zero-energy house located in Southern Italy to minimize thermal and visual discomfort. The optimization problem admits four objective functions (thermal discomfort during winter and summer and visual discomfort due to glare and an inappropriate quantity of daylight) and uses the non-dominated sorting genetic algorithm, implemented in the GenOpt optimization engine through the Java genetic algorithms package, to instruct the EnergyPlus simulation engine. The simulation outcome is a four-dimensional solution set. The building variants of the Pareto frontier adopt diverse and non-intuitive design alternatives. To derive good design practices, two-dimensional projections of the solution set were also analyzed. Finally, in cases of complex optimization problems with many objective functions, optimization techniques are recommended to effectively explore the large number of available building variants in a relatively short time and, hence, identify viable non-intuitive solutions.