Research on Energy Efficiency Optimization Control Strategy of Office Space Based on Genetic Simulated Annealing Strategy

Current energy-saving lighting control algorithms often face the dilemma of local optimality, which limits the energy-saving potential and comfort improvement of indoor lighting systems. The control parameters of the lighting system are optimized using a genetic simulated annealing algorithm to achieve the global optimal solution and enhance energy-saving efficacy in indoor lighting. The local search ability of the algorithm is enhanced by simulated annealing processing of excellent individuals after genetic operation. The genetic probability is adaptively adjusted according to the number of iterations and the fitness of the population, so that the algorithm enriches the population diversity in the early stage and avoids the “premature” convergence of the algorithm. A lamp illuminance model based on an artificial neural network and an indoor natural illuminance model based on a workbench are proposed to evaluate the lighting comfort, which provides a basis for constructing the fitness function of the optimization algorithm. Through the simulation experiment, the genetic simulated annealing algorithm is applied to the lighting scene introduced in this paper and compared with the traditional particle swarm optimization algorithm and genetic algorithm, the lighting energy saving performance is significantly improved.