A short-term wind speed prediction method utilizing rolling decomposition and time-series extension to avoid information leakage

The accuracy of wind speed prediction is crucial for the efficient operation and scheduling of power grids. In recent years, many wind speed prediction methods have been proposed, but the results have always been unsatisfactory, and the model accuracy in experimental testing has always been overestimated. This study focuses on the problem of information leakage caused by the decomposition of the test and general training sets in traditional wind speed prediction methods. Using the original model without decomposition as the standard and the mean average (PMAE) and mean squared (PMSE) errors as evaluation metrics, the overestimation degree of information leakage on the model accuracy was quantified. The results show that when the test set is decomposed together, the accuracy of the model is significantly overestimated. Specifically, the overestimation of PMAE ranges from 40% to 55%, and that of PMSE is from 65% to 85%. In addition, a singular spectrum analysis (SSA) - rolling decomposition (RD) - convolutional neural network (CNN) - bidirectional gated recurrent unit (BiGRU) - attention mechanism (AM) model based on the RD method was proposed. First, SSA was used to denoise the wind speed sequence, and then RD was performed on the original sequence to provide input vectors for the neural network model. Then, the CNN - BiGRU - AM hybrid neural network module predicted the wind speed sequence. Finally, to suppress the impact of boundary effects on the model accuracy, a time-series extension strategy based on neural networks was incorporated into the model. An example analysis indicates that the SSA - RD - CNN - BiGRU - AM model can avoid information leakage compared with other traditional models.