Model Free Adaptive Control for Air-Conditioning System in Office Buildings Based on Improved NSGA-II Algorithm

Driven by the increasing demand for thermal comfort in office building environment and energy saving in building operation process, a model free adaptive control (MFAC) method for air conditioning system based on optimal environmental parameter settings is proposed in this paper. To this end, on the basis of the energy consumption model of common office buildings, the cooling load brought by the fresh air system is considered. The model is combined with the predicted mean vote (PMV) thermal comfort model to constitute an optimization problem. The improved non-dominated sorting genetic algorithm is used to solve the optimization problem to obtain the optimal environmental parameter settings at first. Then, an improved model-free adaptive control method is used for regulation. An adjustment item is added to the estimation algorithm to make the pseudo partial derivative reach the ideal value faster, to reach the optimal environmental parameter setting value faster. Finally, the experimental verification is carried out on the building environment monitoring system of an office building demonstration room, which shows the proposed method can achieve the optimal environment parameter settings, and at the same time can reduce the number of regulation of the air conditioning system and extend the service life of the equipment.