基于集成学习的新能源发电功率预测

NEW ENERGY POWER FORECASTING BASED ONENSEMBLE LEARNING

针对现有新能源发电功率预测方法难以深入挖掘多维变量时序数据特征导致预测精度不佳的问题,提出一种基于集成学习的新能源发电功率预测方法。首先结合3种相关系数与Shapley值法筛选高相关度的相关变量;其次使用扩展因果卷积网络捕捉历史发电功率时序特征,并使用双向门控循环单元网络结合时间模式注意力提取过去和未来的相关变量特征;最后依照Stacking法对不同网络输出进行集成融合。实验表明,该方法在超短期内具有优秀的预测精度,预测结果均优于其他对比模型。

Considering the fact that most of the existing new energy power prediction methods have difficulty in deeply mining the characteristics of time series data of multi-dimensional variables and result in poor prediction accuracy,a new energy power prediction method based on ensemble learning is proposed.Firstly,three correlation coefficients are combined with the Shapley value method to obtain variables with high correlation.Secondly,the dilated causal-convolutional neural network is used to capture the characteristics of historical power time series data,and the bidirectional-gated recurrent unit network is used to extract the variable features from the past and future combined with the temporal pattern attention.Finally,the outputs of different networks are fused according to the Stacking method.Experiments show that the proposed method achieves impressive prediction accuracy in the ultra-short term and the prediction results outperform the other comparison models.