Research on calculation method of indoor set temperature based on FL-NSGA-Ⅱ

This paper takes college classrooms as application objects, takes the thermal comfort and the energy consumption of air conditioning systems as optimization objectives, combines comfort indicators and building bioclimate maps to build a simplified comfort calculation model, and uses the gradient boosted regression (GBR) method to construct a prediction model of energy consumption of air conditioning systems. On this basis, the fuzzy logic (FL) combined with non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) method is used to calculate the Pareto solution set for the indoor set temperature, and the decision tree method is used to determine the unique optimal solution for the indoor set temperature. The thermal comfort and the energy consumption of air conditioning systems under actual working conditions and optimized temperatures are compared. The results show that the model can effectively reduce the energy consumption of air conditioning systems while improving comfort.