碳中和背景下多通道特征组合超短期风电功率预测

A Multi-channel Feature Combination Model for Ultra-short-term Wind Power Prediction Under Carbon Neutral Background

碳中和背景下,风电将成为我国的主导能源之一。随着人工智能技术快速发展,人工神经网络被广泛应用于风力发电功率预测。传统的人工神经网络算法采用固定形式数据集和单一网络结构,限制了整体表达能力,导致超短期风电功率预测由于各种不确定因素造成难以控制的误差。为此,提出一种基于人工神经网络的多通道特征组合模型,用于超短期风电功率预测。首先将数据集进行重新分类,分别输入到3个神经网络,建立3种特征组合形式;再将多通道特征进行拼接融合,并将融合后的特征加入到全连接神经网络中进行功率预测,可消除不同特征之间的干扰,有效学习到长期依赖的数据特征;最后对5个风电场数据进行算法验证。实验结果表明,该方法比单通道模型能够获得更好的预测精度,而且增加了网络稳定性。

Wind power will become one of the dominant power sources of China oriented to carbon neutral.With the rapid development of artificial intelligence technology,artificial neural networks are widely used in wind power generation forecasting.Traditional artificial neural network algorithms use fixed-form data sets and simple network structures,which limits the overall expression ability and results in uncontrollable errors in ultra-short-term wind power forecasting due to various uncertain factors.In this work,a multi-channel feature combination model based on artificial neural network for ultra-short-term wind power prediction was proposed.Firstly,the data were reclassified and input into three neural networks to establish three feature combinations.After that,multi-channel features splicing and fusion were performed.The fused features were added to the fully connected neural network for power prediction,which can eliminate the interference between different features and effectively learn long-term dependent data features.Finally,the algorithm was verified on the actual data of five wind farms.The experimental results show that this method has better prediction accuracy than the single-channel model,and can improve the network stability.