基于GRNN的短期光伏功率预测

Short-Term Photovoltaic Power Prediction Based on GRNN

为提高光伏电站运营当中对输出功率预测的准确度,进一步提升光伏电站的智能化程度,降低光伏电站的运营成本,建立了一种基于GRNN算法的输出功率预测模型。模型利用GRNN神经网络的非线性映射能力预测短期光伏输出功率,可在同等条件下,相较BP神经网络预测算法得到更接近于实际的输出功率值。本模型发挥出GRNN算法结构简单的特性,在实验中实现了较高的预测准确度,同时有助于提高电网运行的稳定性。

In order to improve the accuracy of output power prediction in the operation of photovoltaic power plants,further enhance the intelligence of photovoltaic power plants,and reduce the operating cost of photovoltaic power plants,an output power prediction model based on GRNN algorithm is established.The model uses the nonlinear mapping ability of GRNN neural network to predict the short-term photovo-ltaic output power,which can be closer to the actual output power value than BP neural network predic-tion algorithm under the same conditions.The model takes advantage of the simple structure of GRNN algorithm,and achieves high prediction accuracy in the experiment,which is also helpful to improve the stability of power grid operation.