Short-term PV Power Prediction Based on GA-PSO-BP and Grey Correlation
Photovoltaic(PV)power generation forecast is a more complex nonlinear problem,so this paper first introduces the objective factors that affect PV power generation forecast under normal circumstances,and then conducts grey correlation analysis according to the objective factors,takes the forecast date as a reference,and searches for the date meeting the conditions as a training sample.In order to solve the problem that BP neural net-work is easy to fall into local extremum,particle swarm(PSO)and genetic algorithm(GA)are used to search for optimization.The optimal particles in the particle swarm are sorted,the high-fitness particles are recombined,and vice versa.At the same time,Levy flight and random walk strategies are introduced to enhance the ability of the algorithm to jump out of the local optimal,and the exploration and optimization ability of the adaptive weight factor balance algorithm is added.Simulation examples show that GA-PSO-BP model has better prediction effect and smaller error than BP,PSO-BP and ELM model under different weather conditions,and can effectively avoid prematurity.
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