# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 5 Results
### Models' Adaptation to Wind Speed and Wind Gust Fluctuations  
The experiment investigated how time intervals with different wind speed patterns can impact models' accuracy.  
XGBoost, LTC and Hybrid models' forecasting results together with input wind gust and wind speed features show XGBoost and LTC models' strong ability to adapt to changes in the target values, but both models struggle to accurately capture extreme peaks, which leads to significant deviations (see areas highlighted with yellow in Figure 9). At the end of the year, wind gusts and wind speeds were unprecedentedly high, which contributed to larger fluctuations in predicted values. The hybrid model, however, demonstrates that its predictions are always within the target values. Although there are still extreme cases, the model captures fluctuations more effectively than XGBoost or LTC.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Filtered Wind Speed**} & \multicolumn{2}{c}{**Filtered Wind**} & \multicolumn{2}{c}{**Unfiltered**} \\  & & & & **Speed + Wind Gust** & & \\ \hline
**Models** & **nMAE** & **nRMSE** & **nMAE** & **nRMSE** & **nMAE** & **nRMSE** \\ \hline XGBoost & 0.0943 & 0.1329 & 0.0998 & 0.1352 & 0.1052 & 0.1258 \\ LTC & 0.0948 & 0.1394 & 0.0980 & 0.1406 & 0.1020 & 0.1264 \\ Hybrid & 0.0800 & 0.1210 & 0.0828 & 0.1221 & 0.0856 & 0.1092 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the errors with and without prediction filtering.  
Figure 8: Wind Turbine Eneron E82/2000 power curve.  
As the beginning and the end of the time series show different fluctuations and variance in input data and predictions, the first and last 200 steps of the forecast, along with wind speed and gust, were analyzed in more detail.  
Table 7 shows the Hybrid model's overall accuracy advantage over XBoost and LTC models in the first and last 200-time steps (MAE = 0.07762 in the first 200-time steps and MAE = 0.12671 in the last 200-time steps). These results confirm the Hybrid model's ability to adapt more effectively to different weather conditions and its instability compared to XGBoost and LTC models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**nMAE**} & \multicolumn{3}{c}{**nRMSE**} \\ \hline
**Models** & **XGBoost** & **LTC** & **Hybrid** & **XGBoost** & **LTC** & **Hybrid** \\ \hline First period & 0.10028 & 0.10085 & 0.07762 & 0.13132 & 0.13869 & 0.11518 \\ Last period & 0.16364 & 0.13169 & 0.12671 & 0.21854 & 0.18778 & 0.17142 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Error comparison of the first and last 200-time steps series periods.  
Figure 9: XGBoost, LTC, and Hybrid models’ wind power forecasts over different wind variability intervals.  
Additionally, the bigger first and last periods' nMAE error difference for both XGBoost and Hybrid models indicates that, during periods of high wind speed and gust fluctuations, Hybrid and XGBoost are less effective than LTC. However, over the long term, during calmer periods, Hybrid and XGBoost models demonstrate greater efficiency than the LTC model alone.  
The difference in nRMSE values in calm and high wind speed periods indicates that the Hybrid model handles outliers better than XGBoost but not as well as LTC.