Research on Control Method of Comfortable Lighting and Energy Saving Lighting

In order to solve the complex illuminance modeling and calculation problem in indoor lighting environment, and improve the energy-saving effect of lighting optimization control algorithm. In this paper, a radial basis function neural network (RBFNN) illuminance model is proposed to simplify the calculation process of illuminance and the calculated illuminance value can provide data support for the optimization control calculation as the feedback link of optimization control. In this paper, a genetic simulated annealing algorithm is designed by combining genetic algorithm and simulated annealing algorithm to avoid the problem of traditional control algorithm falling into local optimal solution. Through the simulation verification of three lighting scenes with different personnel distribution, the traditional particle swarm optimization algorithm saves 46.00%, 38.00% and 37.11% energy respectively, while the genetic simulated annealing algorithm saves 47.22%, 46.67% and 41.78 energy respectively. It can be seen that the latter has a better energy-saving effect in the three scenes.