Short-Term Photovoltaic Power Forecasting Based on Elman Neural Network with Fruit Fly Optimization Algorithm

The model based on Elman neural network(NN)with fruit fly optimization algorithm(FOA)is proposed to forecast the short-term photovoltaic(PV) power.Using dynamic recurrent Elman NN,the reasoning and generalization capacity of PV power forecasting model is enhanced,and forecasting accuracy is ensured.The human body amenity is introduced to reduce the number of input vectors.The FOA is used to train the Elman NN,which can make full use of the global optimization performance of FOA and overcome the defects such as local optimal solution,slow convergence speed and complex programming.Finally,in comparison with the simulation results of Elman NN,the numerical results verify the effectiveness and correctness of the proposed mode.