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WIND POWER PREDICTION METHOD BASED ON TIME SEQUENCE AND NEURAL NETWORK METHOD

机译:基于时间序列和神经网络的风电功率预测方法

摘要

Disclosed is a wind power prediction method based on a time sequence and neural network method. The wind power prediction method comprises the following specific steps: step 1, establishing a time sequence model; step 2: establishing a neural network model, and initializing a neural network; step 3, establishing a wind speed prediction model: performing data differential processing according to original data of a wind speed, and establishing a wind speed prediction model based on a time sequence method; step 4, predicting wind power according to the wind speed prediction model and a wind speed-power conversion relationship; and step 5, establishing a wind power prediction model: establishing a prediction model of wind power according to the wind speed prediction model and the wind speed-power conversion relationship, and obtaining a wind power prediction value by using a wind speed prediction value, obtained by using the wind speed prediction model, as an input value of the wind power prediction model. In this manner, prediction errors caused by a nonlinear relationship between the wind speed and the wind power are reduced effectively; and the method is suitable for short-term prediction of wind power.
机译:公开了一种基于时间序列和神经网络方法的风力发电预测方法。风能预测方法包括以下具体步骤:步骤1,建立时序模型;步骤2:建立神经网络模型,并初始化神经网络;步骤3,建立风速预测模型:根据风速原始数据进行数据差分处理,并基于时序方法建立风速预测模型。步骤4,根据风速预测模型和风速-功率转换关系预测风功率。步骤5,建立风电预测模型:根据风速预测模型和风速-功率转换关系建立风电预测模型,并利用风速预测值得到风电预测值,得到通过使用风速预测模型作为风能预测模型的输入值。通过这种方式,有效地减少了由风速和风力之间的非线性关系引起的预测误差。该方法适用于风电的短期预测。

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