改进的粒子群算法在太阳能光伏发电资料同化中的应用研究

资料同化是目前太阳能光伏发电预测研究的一个关键和难点.近年来,遗传算法和粒子群算法等智能优化算法被引入到四维变分同化中.针对基于分子运动论的粒子群算法(MPSO)在处理大量数据时速度慢的不足,该文提出了并行分子运动论粒子群算法(PMPSO),并行计算的基本思想是将粒子群分成N个子集,每个子集交给一个线程控制,同时进行粒...

Research on data assimilation in solar photovoltaics power generation based on improved PSO algorithm

Data assimilation is a key and difficult point in the research of solar photovoltaic power generation prediction. Intelligent optimization algorithms such as genetic algorithm and particle swarm optimization algorithm are introduced into the four-dimensional variational assimilation. Aimed to slow speed of Particle Swarm Optimization algorithm based on Molecular Motion Theory (MPSO) when dealing with problems with large amounts of data， in this paper， a Parallel Modified Particle Swarm Optimization (PMPSO) is proposed， using parallel computing to shorten running time. The basic idea of parallel computing is to divide the particle swarm into N subsets， with each subset given to a thread for control， and iteration operation is carried out at the same time to improve the processing speed of the algorithm. After each iteration， the data of the elite particles in each subset will be transferred to the public part， and then the next iteration will be carried out to make the information exchange among each subset to increase the diversity. Results are applied to data assimilation in numerical weather forecasting， and compared with MPSO， Particle Swarm Optimization with Dynamic Inertia Weight and Acceleration Factor (PSOCIWAC) and Particle Swarm Optimization with Time Varying Constrict Factor (PSOTVCF) in accuracy and time， the experimental results showed that based on the PSOCIWAC and PSOTVCF methods， the convergence accuracy of PMPSO method is improved by 10 000 and 100 times， respectively and it also have great advantages in time.