Multi-objective optimization of energy performance for a detached residential building with a sunspace using the NSGA-II genetic algorithm

•Passive solar design optimization using NSGA-II of a detached building with a sunspace.•Optimization objectives: heating energy, cooling energy, thermal comfort.•Variable parameters: WWR, glazing type, façade wall constructions, window shading.•Sensitivity analysis prior to the optimization.•Optimization – Scenario 1: 4245 iterations and Scenario 2: 4393 iterations.•Window-to-wall ratio is the element of passive solar design that influences energy performance the most. This paper discusses a performed optimization of the structural and architectural parameters of a detached passive building with a sunspace using a non-dominant sorting genetic algorithm (NSGA-II). The building optimization is performed on the model of a building located in Niš, Serbia, a city with a Cfa climate according to the Köppen classification. The optimization is based on the NSGA-II algorithm and was run using DesignBuilder software package coupled to EnergyPlus™ dynamic building energy simulation software. The defined variable optimization parameters (X1-X10) include parameters of passive solar design, such as window-to-wall ratio (WWR) of each individual building façade, type of glazing, different façade wall constructions, and window shading system on the façades. Prior to the optimization itself, a sensitivity analysis of parameters X1-X10 was conducted in order to identify the parameter that has the most influence on heating and cooling energy expenditure and on thermal comfort. The Latin hypercube sampling (LHS) method is used to create iterations. Optimization objectives are defined for two scenarios. Scenario 1 considers the minimum energy required for heating and the minimum energy required for cooling of a detached passive residential building with a sunspace, while Scenario 2 considers the minimum energy required for heating and the minimum number of discomfort hours in such a building. The results of the optimization are obtained through NSGA-II iterations according to the predefined optimization objectives and by varying the given structural and architectural parameters of the building. The results are presented as a Pareto front – a set of optimal solutions and optimal characteristics of the detached passive solar building. For Scenario 1, the optimization yielded 63 Pareto solutions after 4245 iterations. For Scenario 2, it yielded 25 Pareto solutions after 4393 iterations. Based on the hypervolume indicator (HV), it can be concluded that multi-objective optimization