基于组合神经网络的分布式光伏超短期功率预测方法

Distributed photovoltaic ultra-short-term power prediction method based on combined neural network

分布式光伏电站在电力系统中的渗透率逐年升高,为保障电网安全稳定运行,提出一种基于组合神经网络的分布式光伏超短期功率预测方法。首先利用一维卷积神经网络(1DCNN)与长短时记忆(LSTM)神经网络构建1DCNN&1DCNN-LSTM组合神经网络模型,获取多位置数值天气预报(NWP)信息与历史功率信息;然后利用组合神经网络模型进行空间相关性光伏功率预测与时间序列预测,并在组合神经网络模型中加入全连接神经网络(FCNN),利用全连接神经网络对2种预测结果进行学习与权重分配,实现了分布式光伏发电功率的超短期预测。采用河北某光伏电站实测数据进行验证,验证结果表明,该方法能够有效提高分布式光伏预测精度,具有一定的实用价值。

The penetration rate of distributed photovoltaic power stations in the power system is increasing year by year,to ensure the safe and stable operation of the power grid,a distributed photovoltaic ultra-short-term power prediction method based on combined neural networks is proposed.Firstly,a 1DCNN&1DCNN-LSTM combined neural network model is constructed by using 1D convolutional neural network(1DCNN)and long short-term memory(LSTM)neural networks,to obtain multi location numerical weather prediction(NWP)information and historical power information,using combined neural network model for spatially correlated photovoltaic power prediction and time series prediction;and a fully connected neural network(FCNN)is added to the combined neural network model,which is used to learn and assign weights to the two prediction results,achieving ultra-short-term prediction of distributed photovoltaic power generation.The validation was conducted using measured data from a photovoltaic power station in Hebei,and the results showed that this method can effectively improve the accuracy of distributed photovoltaic prediction and has certain practical value.