基于遗传模拟退火算法的室内照明节能控制方法研究北大核心CSCD

目前的节能照明控制算法仍有陷入局部最优的问题。为了寻求全局最优解,提高室内照明的节能效果,设计一种遗传模拟退火算法对照明系统的控制参数进行优化求解。该算法通过在遗传操作后对优秀个体进行模拟退火处理,增强了算法的局部搜索能力。根据迭代的次数和种群的适应度对遗传概率进行自适应调节,使得算法在前期丰富种群多样性,避免算法“早熟”。提出基于人工神经网络的照度模型来计算室内照度分布,对照明舒适度进行评估,为构造优化算法的适应函数提供了依据。通过仿真实验,在本文介绍的照明场景应用遗传模拟退火算法,并与传统粒子群算法和遗传算法进行比较,其照明节能性能分别高出5.30%和13.61%。

Indoor lighting energy-saving control method based on genetic simulated annealing algorithm

The current energy-saving lighting control algorithm still falls into the problem of local optimum. In order to seek for the global optimal solution and improve the energy-saving effect of indoor lighting, a genetic simulated annealing algorithm was designed to optimize the control parameters of lighting system. The local search ability of the algorithm was enhanced by simulated annealing treatment of excellent individuals after genetic manipulation. According to the number of iterations and the fitness of the population, the genetic probability was adaptively adjusted so that the algorithm could enrich the population diversity in the early stage and avoid the prematurity of the algorithm. An illumination model based on artificial neural network was proposed to calculate the indoor illumination distribution and evaluate the illumination comfort, which provided a basis for constructing the fitness function of optimization algorithm. Through simulation experiments, the genetic simulated annealing algorithm was applied in the introduced lighting scenes, and compared with the traditional particle swarm algorithm and genetic algorithm, the lighting energy-saving performance was 5.30% and 13.61% higher respectively.