电测与仪表Issue(12)：120-124,128,6.
基于FOA-Elman神经网络的光伏电站短期出力预测模型
Short-Term Photovoltaic Power Forecasting Based on Elman Neural Network with Fruit Fly Optimization Algorithm
摘要
Abstract
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.关键词
光伏电站/出力预测/Elman神经网络/FOA算法Key words
photovoltaic power generation/power forecasting/Elman neural network/fruit fly optimization algorithm分类
信息技术与安全科学引用本文复制引用
韩伟,王宏华,杜炜..基于FOA-Elman神经网络的光伏电站短期出力预测模型[J].电测与仪表,2014,(12):120-124,128,6.基金项目
江苏省研究生培养创新工程 （）
