Multi-objective optimization of window opening and thermostat control for enhanced indoor environment quality and energy efficiency in contrasting climates

Abstract

In this study, a triple-objective model-based optimization of a controlled opening window's specifications and thermostat control setpoints was investigated. Numerical simulations were performed employing EnergyPlus, and multi-criteria optimization of objective functions and decision parameters was accomplished by jEPlus + EA using Non-dominated Sorting Genetic Algorithm (NSGA-II). Controlled window optimization was carried out for a typical classroom, and the results were evaluated for two contrast climates. The decision variables are indoor temperature set points for opening control, thermostat set points, and the window opening area. The annual energy demand, the average CO2 concentration, and the predicted percentage of dissatisfaction (PPD) were also regarded as objective functions to be simultaneously minimized. Max-min normalization and selecting final answers from the Pareto front were performed by the weighted sum method. By operating the suggested parameters, executing the optimized control strategy on the window opening resulted in indoor environmental quality improvement and a substantial decrease in energy demand, which had been selected as objective functions. The results show that using a controller based on indoor temperature instead of time significantly improved indoor air quality but greatly increased energy consumption. By performing optimization, in more than 50% of the time, all objective functions were controlled simultaneously for both selected climates. In total, the optimized window and thermostat controller performed desirable in both climates.