为了响应国家"双碳"目标,针对风电功率预测误差影响电网安全稳定运行的问题,提出一种基于双重注意力机制改进的CNN-BiLSTM初步预测和LightGBM误差修正的组合预测模型.该模型首先利用卷积神经网络(Convolutional neural network,CNN)与注意力机制结合构成特征注意力模块自适应提取风电功率重要特征,然后利用双向长短期记忆网络(Bi-directional long short-term memory,BiLSTM)与注意力机制结合构成时间注意力模块对风电功率进行初步预测,最后利用LightGBM构造误差修正模型,对初步预测结果进行修正.使用平均绝对误差(Mean absolute error,MAE)、均方根误差(Root mean square error,RMSE)和确定系数(R2)作为试验评价指标,结果表明,该组合模型预测效果明显优于BiLSTM、CNN-BiLSTM等模型.
Ultra-short-term Wind Power Prediction Based on Dual-stage Attention Mechanism CNN-BiLSTM and LightGBM Error Correction
In order to respond to the national goal of "double carbon",a combined prediction model based on CNN-BiLSTM with dual-stage attention mechanism for preliminary prediction and LightGBM for error correction is proposed to address the problem of wind power prediction errors affecting the safe and stable operation of power grids.The model first uses a convolutional neural network(CNN) combined with an attention mechanism to form a feature attention module to adaptively extract important features of wind power,then uses a BiLSTM network combined with an attention mechanism to form a temporal attention module to make preliminary predictions of wind power,and finally uses LightGBM to construct an error correction model to correct the preliminary prediction results.Using the mean absolute error(MAE),root mean square error(RMSE) and coefficient of determination(R2) as experimental evaluation metrics,the results show that the combined model predicts significantly better than BiLSTM and CNN-BiLSTM models.
Wind power predictionattention mechanismconvolutional neural networklong short-term memory networkerror correctionLightGBM
万方数据
维普
