Abstract:Accurate prediction of wind turbine metrics is important for accurate control of turbines and the regulation of grid supply and demand. The task of forecasting these indicators can be abstracted as a task of wind power time series forecasting. Currently, deep learning models are mainly used in time series prediction models, but the strong volatility and randomness of wind power time series often prevent most models from effectively capturing the complex evolutionary characteristics of the data. To address these issues, a wind power time series forecasting method based on a progressive decomposition architecture is proposed, which first applies a neural network pooling decomposition method to simplify complex dependencies and then applies an attention mechanism to learn long-term trends. Subsequently, a multivariate fusion capture module is employed to enhance the overall multivariate correlation mining ability of the network, and it fuses the trend term and the period term to make accurate forecasts of the wind power time series. Finally, the trend and period terms are fused to make accurate forecasts for wind power time series. Experimental results show that this method can achieve up to a 24% reduction in mean squared error (MSE) for wind power time series forecasting compared to baseline models. It also exhibits stable improvements in predictive performance across multiple forecasting lengths while significantly outperforming similar models in computational efficiency.