*Result*: Short-Term Wind Power Forecasting: A Novel Enhanced Gate-Based Deep-Learning Model Containing a Metaheuristic Algorithm with an Intelligent Position Navigation Optimization Strategy.

*Wind energy generation technology has facilitated the diversification and transformation of energy sources, while also improving the sustainability and cleanliness of energy. However, the complex and variable external environment introduces significant uncertainty in wind speed, leading to considerable fluctuations in wind power output. The large-scale integration of wind power into the grid can compromise the stability of grid operations, making forecasting techniques an effective solution to this challenge. In this study, we propose a novel enhanced gate-based long short-term memory (EHLSTM) model designed to enhance the model's ability to extract important information from raw input data, thereby improving the long short-term memory's (LSTM's) performance in handling complex and highly volatile wind power data. First, t -distributed stochastic neighbor embedding is employed for dimensionality reduction of the wind power data set to alleviate the processing difficulty for the model. Next, the EHLSTM is constructed based on traditional LSTM architecture, and intelligent polar lights optimization (IPLO) is introduced to enhance the predictive performance of the EHLSTM model. Finally, the proposed model and algorithm are validated using real-world wind power data sets. The results indicate that under varying seasonal conditions, the R2 value of the IPLO-EHLSTM model exceed 98%, with the mean absolute percentage error maintained below 0.4%. This demonstrates that the model accurately captures the highly volatile nature of wind power output sequences. [ABSTRACT FROM AUTHOR]*