计及相似日的LSTM光伏出力预测模型研究

STUDY ON LSTM PHOTOVOLTAIC OUTPUT PREDICTION MODEL CONSIDERING SIMILAR DAYS

为了提高光伏电站输出功率的预测精度,该文构建基于灰色关联度分析法(GRA)和长短期记忆神经网络(LSTM)的光伏发电组合预测模型。在运用GRA方法确定影响光伏出力的主要气象因素和选定待预测日的相似日的基础上,利用相似日的气象参数和实际发电量分别训练BP神经网络和LSTM神经网络,构建基于GRA的光伏出力智能预测模型,并在云南某光伏电站得到很好的应用。对比传统的单一预测模型和BP神经网络与GRA的组合模型,考虑相似日的LSTM预测模型的精度明显提升,可很好地满足相关要求,具有广阔的应用前景。

To improve the prediction accuracy of PV plant’s power output,a hybrid grey relational analysis(GRA)and long-short term memory(LSTM)neural network model was established in this study.Firstly,main meteorological factors affecting the photovoltaic output were identified and the similar days of the days to be predicted were determined by GRA method.Next,BP neural network model and LSTM neural network model were trained by using the meteorological parameters and actual power generation of similar days.Finally,the intelligent prediction model based on GRA method was developed,which was applied in the photovoltaic power station in Yunnan.Compared with traditional single prediction model and the combined model of BP and GRA,the accuracy of LSTM prediction model considering similar days is significantly improved,which meets the relevant requirements and owns good application prospect.