A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting

Wind power generation plays a crucial role in promoting the transformation and advancement of the power industry and fostering sustainable development in society. However, wind power generation is susceptible to external factors, exhibiting significant volatility and randomness, which can adversely affect the stable operation of the power grid. The uncertainty associated with wind power generation can be effectively addressed through wind power prediction technology. While numerous wind power prediction methods have been developed, challenges remain, including imperfect data processing mechanisms and the optimization of model parameters, which hinder the effective utilization of wind power generation. To overcome these challenges, this research proposes a novel deep learning-based evolutionary model with a potential attention mechanism and memory decay-enhancement strategy for short-term wind power point-interval forecasting. The proposed model demonstrates high prediction stability and accuracy for both point values and intervals of short-term wind power, even under complex environmental and multi-seasonal conditions. The effectiveness of the proposed methods and strategies are validated using a measured dataset from the La Haute Borne wind farm in France. The results consistently show that, in complex environmental scenarios, the point prediction evaluation index of determination coefficient exceeds 90% and the interval prediction evaluation index of PI coverage probability exceeds 80%. Accurate short-term wind power point-interval forecasting contributes to enhancing the stable operation of the power system and improving the efficiency of wind energy utilization.