A novel EMD and causal convolutional network integrated with Transformer for ultra short-term wind power forecasting

Accurate wind power forecasting can enhance the safety, stability, economy and controllability of the power system. Traditional physical methods and statistical methods are easily affected by data quality and extraction methods in wind power forecasting. The commonly used recursive neural network method may have memory decline phenomenon in wind power forecasting and does not support parallel calculation, thus limiting the forecasting accuracy. To solve the above problems, in this paper, a wind power forecasting method based on EMD-CCTransformer is proposed. The network model is based on an encoder–decoder structure, where the encoder is used to parse historical wind power sequences, the decoder generates…