# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 6 Conclusions  
### Summary of Findings  
In the course of this research, a one-year dataset was utilized for models' training and evaluation. Based on long-term meteorological (including wind speed measurements) observations in Lithuania, this duration was deemed sufficient to develop predictive models and assess their accuracy. The data also enabled an analysis of the seasonal effects on the relationship between wind speed and wind power generation. Historical meteorological records indicate that the highest wind speeds and most frequent gusts occur during January, November, and December. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Earnings (Eur in Thousands)** \\ \hline XGBoost & 188 \\ LTC & 201 \\ Hybrid & 226 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Total earnings comparison. Figure 16: Composition of potential earnings. Incorporation of lags in wind power generation data enabled the development of models with higher forecasting accuracy. This strategy, while effective, presents certain challenges, as it reduces reliance on factual historical data and introduces a recursive forecasting structure. In such cases, prediction errors can propagate through subsequent time steps, potentially degrading model performance and adversely affecting long-term forecast reliability. Nevertheless, for 24-h forecasting, the careful selection of lag sets mitigated error accumulation. As a result, the models remained stable and accurate, even when part of the forecast became dependent on previously predicted values. Based on comparative analysis, the proposed Hybrid (XGBoost + LTC) model demonstrated the highest overall forecasting accuracy (nMAE of 0.0856 and nRMSE of 0.1092) and achieved the lowest nMAE and nRMSE values with different wind speed patterns. This indicates its superior adaptability to varying weather conditions and stability compared to XGBoost and LTC models. While XGBoost performed well during calmer periods, it showed a significant increase in error during high wind fluctuation intervals. Conversely, the LTC model maintained more consistent performance across both periods, suggesting better robustness to outliers. However, the Hybrid model effectively balanced both adaptability and precision, making it the most efficient model for wind power forecasting across diverse meteorological scenarios. Furthermore, the Hybrid model showed stable performance in a 72-h forecasting period, supporting its potential for reliable medium- to long-term wind power predictions. A comparative evaluation of forecasting models, based on findings from the literature and focused on a 24-h prediction horizon with a 1-h step, highlights the strengths and trade-offs among statistical, machine learning, and hybrid approaches. While statistical models such as SARIMA, SARIMAX, and the Markov chain achieved the lowest average nMAE (0.05265), they exhibited more dispersed errors with an average nRMSE of 0.12567. Machine learning models like LSTM, SVR, ELM, and 1D-CNN demonstrated improved short-term accuracy (average nMAE of 0.05805 and nRMSE of 0.09791), though they struggled with rapid fluctuations. In contrast, the proposed Hybrid (XGBoost + LTC) model, with an nMAE of 0.0856 and nRMSE of 0.1092, outperformed the broader group of hybrid models (average metrics of about 0.11284 for nMAE and 0.12961 for nRMSE) and offered a balanced compromise--delivering improved trend detection and more consistent error behavior across varying conditions. These findings underscore the Hybrid model's practical value in real-world wind power forecasting scenarios. The economic evaluation, based on hourly NordPool electricity market data and wind power forecasting results, highlights the revenue potential and stability associated with different forecasting models. Forecasts generated by the proposed Hybrid (XGBoost + LTC) model resulted in an estimated revenue increase of 16.47% compared to the XGBoost model and 11.01% compared to the LTC model.