Abstract
ABSTRACT There have been more concerns over energy resource depletion and indoor comfort improvement along with the increased time spend in a building. Building operation requires more energy in the spaces of high occupancy density in public buildings in summer because most energy has been consumed to make the indoor environment comfortable. Therefore, there is a conflicting issue for optimization, which is minimum energy consumption vs. maximum indoor comfort. In this paper, the indoor comfort model was established based on the weights of the thermal environment and air quality. The indoor air temperature, indoor relative humidity and indoor CO2 concentration as KPI (Key performance indicators) are optimized dynamically according to different outdoor meteorological parameters and different indoor occupancy density by an improved multi-objective optimization method of Non-dominated Sorting Genetic Algorithm II (NSGA-II). A single suitable solution from the non-inferior solutions is selected by the method of fuzzy comprehensive decision. The single solution of those KPI can be used as the set point of air-conditioning controller. A case study was carried out and the corresponding simulation of optimization of energy and comfort was presented. The results showed that the methodology can achieve the maintenance of indoor comfort and energy consumption reduction.
Published Version
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