考虑特征重组与改进Transformer的风电功率短期日前预测方法

Short-term Day-ahead Wind Power Prediction Considering Feature Recombination and Improved Transformer

短期日前风电功率预测对电力系统调度计划制定有重要意义,该文为提高风电功率预测的准确性,提出了一种基于Transformer的预测模型Powerformer。模型通过因果注意力机制挖掘序列的时序依赖;通过去平稳化模块优化因果注意力以提高数据本身的可预测性;通过设计趋势增强和周期增强模块提高模型的预测能力;通过改进解码器的多头注意力层,使模型提取周期特征和趋势特征。该文首先对风电数据进行预处理,采用完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)将风电数据序列分解为不同频率的本征模态函数并计算其样本熵,使得风电功率序列重组为周期序列和趋势序列,然后将序列输入到Powerformer模型,实现对风电功率短期日前准确预测。结果表明,虽然训练时间长于已有预测模型,但Poweformer模型预测精度得到提升;同时,消融实验结果验证了模型各模块的必要性和有效性,具有一定的应用价值。

Short-term day-ahead wind power forecasts are important for power system dispatch planning.In order to improve the accuracy of wind power prediction,this paper proposes a Transformer-based prediction model Powerformer.This model mines the temporal dependence of sequences through the causal attention mechanism,which is optimized to improve the predictability of the data itself through the De-stationary module.The predictive power of the model is improved by designing the trend enhancement and the cycle enhancement modules.By improving the multi-headed attention layer of the decoder,the model extracts the cycle features and the trend features.In this paper,we first preprocess the wind power data.We decompose the wind power data series into the eigenmodal functions with different frequencies and calculate their sample entropy by using the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)so that the wind power series are reorganized into the cycle and the trend series.Then,the series are put into the Powerformer model to achieve the accurate prediction of the wind power in a short term.The results show that,although the training time is longer than the existing prediction models,the prediction accuracy of the Poweformer model is better improved.The necessity and effectiveness of each of the modules in this model are verified by the ablation experiments,which has a certain application value.