Multi-objective optimization of setpoint temperature of thermostats in residential buildings

Determination of the optimum setpoint temperature of thermostats in various climates is a problem in air conditioning of residential buildings. In this paper, a new method is developed to optimize the setpoint temperature of thermostats in different climates of Iran. Design variables in the optimization process are heating setpoint, cooling setpoint, thickness, and thermal conductivity of insulations in the building envelopes. The optimization goals are minimizing energy consumption and cost of insulations in addition to maximizing thermal comfort of occupants. Thus, the static payback period (SPP) and the predicted percentage dissatisfied (PPD) indices are selected as objective functions which should be minimized in the optimization process. The methods applied to attain these objectives are numerical modeling by EnergyPlus software, Grouped Method of Data Handling (GMDH) type of Artificial Neural Network (ANN), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Therefore in this process, first, EnergyPlus is used to train the neural network. Afterward, the GMDH-type neural network is applied to derive polynomials computing the objective functions from the design variables. Then, Pareto optimal points for the objective functions are obtained through using these polynomials and NSGA-II multi-objective optimization. Finally, the optimum design point is selected for different cities. According to the results, type and thickness of insulation integrated in the building envelopes affect the static payback period and thermal comfort of occupants. For all the climates of Iran, the most appropriate insulation is XPS and the optimum heating setpoint of thermostat is 22 °C. Also, the optimum value for the cooling setpoint pertains to the type of climate, so that this value for Bandar Abbas, Yazd, Tehran, Rasht and, Tabriz is, respectively, equal to 24.5, 24.7, 25.2, 25.3, and 25.6 °C. Moreover, thermal comfort of occupants increases with thickness of insulation, except for Bandar Abbas whose PPD is almost constant. The most value of PPD reduction with insulation thickness is related to Tehran where by increasing the insulation thickness from 1 cm to 5 cm, PPD decreases up to 53%. © 2022 Elsevier B.V.