# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 5 Conclusions  
This study introduces a novel xLSTM architecture for wind power forecasting that addresses fundamental limitations in conventional recurrent neural networks. The xLSTM model's key innovations, which are exponential gating with memory mixing and matrix memory structures, enable more effective processing of temporal dependencies in wind power data. Empirical evaluation using wind turbine SCADA data demonstrates the xLSTM model's superior predictive performance across diverse operational conditions, achieving comprehensive metrics of \(R^{2}\) = 0.923, MAPE \(\epsilon\) = 8.47%, RMSE \(\zeta\) = 34.6 kW, and MAE \(\sigma\) = 36.2 kW across all wind speed regimes. The xLSTM architecture demonstrates particularly strong performance in high wind speed conditions (>12 m/s), attaining an \(R^{2}\) value of 0.954 and MAPE of 4.21%. This enhanced accuracy in the power curve's plateau region provides significant value for operational planning and grid integration during periods of maximum generation. Seasonal analysis confirms the model's robust temporal pattern recognition capabilities, with consistently strong predictive performance across all seasons (\(R^{2}\) values: 0.963-0.987) and effective modeling of both unimodal winter distributions and more complex bimodal patterns characteristic of transitional seasons. The comparative analysis with established forecasting models demonstrates that xLSTM consistently outperforms traditional approaches like ARIMA (by 35.3%), standard recurrent architectures like LSTM and GRU (by approximately 6-7%), and even advanced models such as Transformers (by 2.3%) across all evaluation metrics. These quantitative improvements, particularly the 21.1% reduction in MAPE compared with conventional LSTM, validate the architectural enhancements of exponential gating and matrix memory structures as significant contributions to the field of wind power forecasting. The architectural components work synergistically, with mLSTM enhancing parallel sequence processing and information flow while sLSTM with exponential gating enables more effective revision of stored information through scalar cell states. This complementary integration provides a robust framework for modeling the complex, non-linear relationships inherent in wind power generation. The xLSTM model demonstrates significant improvements over conventional approaches in computational efficiency, prediction accuracy, and generalizability across varying temporal and meteorological conditions. Future work will address the limitations of our current study by expanding both the spatial and temporal dimensions of our analysis. Future work will extend the current forecasting framework of xLSTM to multiturbine wind farm settings, which will enable investigation of inter-turbine interactions, wake effects, and spatial dependencies that affect aggregate power generation. The xLSTM model's temporal robustness will be enhanced through the incorporation of physics-based constraints and meteorological features that may improve long-term stability and performance under extreme weather conditions. Efficient transfer learning mechanisms will be developed to continuously update model parameters as new operational data becomes available, reducing the need for complete retraining while maintaining prediction accuracy across extended operational periods. The generalizability concerns identified in the current implementation will be collectively addressed by these developments while preserving the computational efficiency advantages of the xLSTM architecture. Conceptualization, Z.B. and G.L. ; methodology, Z.B. ; validation, Z.B. and G.L. ; formal analysis, Z.B. ; investigation, Z.B. ; resources, G.L. ; data curation, G.L. ; writing--original draft preparation, Z.B. ; writing--review and editing, G.L. ; visualization, Z.B. ; supervision, G.L. ; project administration, G.L. ; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.