Thermal comfort in offices: comfort values and optimization of indoor control variables

In order to search for reasonable air-conditioned indoor control variables and save energy consumption and meet to need of personal thermal comfort, a method which is based on numerical simulation is employed to optimize indoor control variables. Computational fluid dynamics (CFD) is used to describe thermal state of office. An optimal method is proposed in this paper, dual neural network model is firstly used to acquire reliable information, data from CFD model are pre-processed, and the remaining data are used to train artificial neural networks (ANN), then CFD model is replaced by ANN model to reduce computational cost when is optimized, indoor control variables are optimized by genetic algorithm. Simulation results show that indoor thermal comfort is improved obviously, and the energy cost is decreased accordingly.