유전알고리즘을 이용한 오피스 건물의 피크전력 감소를 위한 일일 예냉설정온도 최적화 연구

Abstract

In this paper, the peak electric power of an office building was reduced with a precooling strategy. Precooling was conducted two hours before work time. Set-point temperatures for precooling were optimized using a genetic algorithm to minimize daily peak electric power. Searching for optimized set-point temperature was conducted with an objective function that was developed with a multiple regression model. The regression model used dry-bulb temperature, relative humidity, and the set-point temperature as the input parameters, and set daily peak power of the building as the output parameter. Model data were collected from ‘EnergyPlus’, a building energy simulation program. The applied building was ‘Medium Office’ provided by EnergyPlus. Based on our results, there was a 0.71~5.64% reduction in daily building peak power with the application of the optimized precooling setpoint temperature. In addition, the reduction of electricity cost due to the reduction of peak electric power was observed.