国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

To address the issue of peak load demands on power grids during summer caused by office building air conditioning systems, a demand response-based Distributed Model Predictive Control (DMPC) cooling strategy for multi-zone systems was proposed. The study used a physical model and a mathematical model of energy consumption for the air conditioning systems in five zones of an office building in Xi"an, validating their accuracy. The multi-zone air conditioning system simulation aimed to minimize operational energy consumption and the temperature deviation from set points. The dung beetle algorithm was selected for optimization, overcoming its slow global search, premature convergence, and susceptibility to local optima through chaotic mapping for population initialization, helical search strategies for foraging and breeding behaviors, and random perturbations with adaptive factors to improve exploratory behavior. Enhanced with these modifications, the dung beetle algorithm improved the rolling optimization of DMPC, which outperformed PID temperature feedback control, increasing response speeds by 8.91%, 8.65%, 12.04%, 5.79%, and 1.79% in the respective zones. Additionally, demand response strategies incorporating time-of-use electricity rates suggested temperature and start-stop optimization strategies to shift peak loads, achieving peak load transfer rates of 27.29% and 29.16% with the precooling start-stop strategies, effectively redistributing peak cooling loads to off-peak periods and alleviating pressure on the power grid.