# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 4 Results and Discussion
### xLSTM Results for Wind Energy Forecasting
#### 4.1.2 xLSTM Results  
The xLSTM model is configured for short-term wind power forecasting with a temporal horizon of 1-6 h. In the experimental setup, the xLSTM model processes input sequences of length 8, which represent 80 min of SCADA data at 10 min sampling intervals, to generate predictions for the next time step. This study evaluates multistep forecasting capabilities at incremental steps between 1-6 h (6-36 prediction intervals), which corresponds to the critical short-term operational planning window for wind farm management and grid integration. The power-wind speed characteristic exhibits the expected sigmoidal pattern typical of wind turbine performance.At low wind speeds (<5 m/s), minimal power generation occurs as these velocities approach the cut-in threshold of the turbine. The power generation increases rapidly in mid-range wind speeds (between 5-12 m/s), displaying a near-cubic relationship with wind velocity, which aligns with fundamental aerodynamic principles. At higher wind speeds (>12 m/s), the power output plateaus around 3500 kW, indicating the rated capacity of the turbine has been reached, after which pitch control mechanisms maintain relatively constant power output despite increasing wind speeds. The xLSTM-predicted power values demonstrate the xLSTM model's capability to capture underlying patterns in the actual power production data, as shown in Figure 2. xLSTM prediction results closely follow the central tendency of the actual power measurements while exhibiting less extreme deviations than the SCADA values. This suggests that the xLSTM model effectively captures the primary relationship between wind speed and power output while filtering out some of the stochastic variations present in the real-world measurements. In the mid-range wind speeds (7-12 m/s), where the power curve exhibits its steepest gradient, both the SCADA measurements and xLSTM predictions display the greatest variance. This heightened variability is attributable to the sensitivity of power production to small fluctuations in wind speed within this transitional region. The xLSTM model appears to slightly overestimate power production at the lower end of this range (7-9 m/s) and underestimate it at the higher end (10-12 m/s), indicating potential areas for model refinement. The convergence of all three data series at low (<5 m/s) and high (15-20 m/s) wind speeds demonstrates the xLSTM model's robust performance at the extremes of the operational wind speed range. The clustering of points near the theoretical maximum capacity indicates that the xLSTM model effectively captures the power limitation mechanisms that prevent exceeding the rated capacity during high wind conditions. The comparative analysis of wind velocity distributions across seasonal patterns reveals insights into the predictive capabilities of the xLSTM model relative to actual SCADA measurements, as shown in Figure 3. The winter distribution exhibits a predominant unimodal pattern with peak probability density occurring at approximately 7-8 m/s, as shown in Figure 3a. Both SCADA measurements and xLSTM predictions demonstrate remarkably similar distribution shapes, with the model accurately capturing the central tendency and overall distribution profile of the empirical data. The close alignment of the density curves indicates high predictive fidelity during cold-weather operational conditions, which typically feature more consistent wind patterns. The slight variations observed at wind speed ranges of 5-7 m/s and 12-14 m/s are minimal and do not represent statistically significant deviations. Spring distributions reveal a more complex bimodal pattern with a primary concentration at 3-4 m/s and a secondary peak at approximately 8-10 m/s, as shown in Figure 3b. This bimodality reflects the transitional nature of spring weather patterns. The xLSTM model demonstrates good predictive performance across most of the distribution, though minor discrepancies are evident in the relative heights of the primary peaks.