基于相似日和IGA-BP的光伏发电功率预测方法研究

准确预测光伏发电功率有利于并网后电网调度管理,现阶段光伏发电功率预测存在精度较低和对不同天气类型的适应性弱的问题。探索了一种相似日与免疫遗传神经网络(IGA-BP)结合的预测方法:基于天气类型、温度及风速,结合灰色关联度和余弦相似度指标构建气象相似日判别模型;以相似日气象特征向量为输入,建立IGA-BP功率预测模型。利用实测数据对比分析所提IGA-BP模型与GA-BP、BP模型的预测精度,结果为:在不同天气类型下IGA-BP模型具有较高精度,其RMSE平均值为14.142%,TIC平均值为0.017 58,均优于其他对比模型。表明IGA-BP模型能够提高功率预测精度,且具有较高的适应性。

A Study on the Forecasting Method of Photovoltaic Power Generation Based on Similar Day and IGA-BP

Accurate prediction of photovoltaic power generation is conducive to improvement of dispatching management for the PVconnected power grid,but the present PV prediction method is low in accuracy and weak in adaptability to different weather types. This paper explores and presents a prediction method which combines the similar day and immune genetic neural network(IGA-BP).Under the frameworks of the method,based on the weather type,temperature and wind speed, a weather similarity day discrimination model combining grey correlation analysis and cosine similarity index is constructed;and with the meteorological characteristic vector of similar days as the input,the IGA-BP power prediction model is established. The prediction accuracy of the IGABP model proposed in the paper is compared with that of the GA-BP and BP models by using the measured data. The results show that the IGA-BP model has high accuracy under different weather types,with an average RMSE of 14.142% and an average TIC of 0.01758 respectively,which are better than other models,indicating that the IGA-BP model can improve the power prediction accuracy and also has high adaptability.