# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 2 Existing Wind Power Forecasting Solutions and Their Limitations
These models can include local atmospheric stability indicators and past data from both real sensors and previous forecasts, helping to improve short-term accuracy while keeping computations manageable [22]. Another effective method is ensemble learning, where multiple models analyze different time intervals or frequency patterns in the wind signal [28]. Studies on newly built offshore wind farms show that these techniques can achieve root mean squared errors as low as 8% of the farm's total capacity [29]. Some researchers also use domain adaptation, which allows models trained in one wind farm to be applied in another one when there is insufficient direct training data [30]. Others apply advanced Bayesian filters to better handle uncertainty in meteorological data assimilation [31]. Despite these improvements, hybrid forecasting models also come with several challenges. Many of these methods require significant computational resources, making them difficult to deploy in real-time operational settings [22]. The complexity of integrating physics-based simulations with machine learning increases the risk of overfitting, especially when training data is limited or noisy [30]. Additionally, ensemble learning and metaheuristic optimization approaches can be difficult to interpret, limiting their adoption in industry applications where explainability is important [26]. Forecast accuracy over a given time frame can be assessed in two ways, depending on the relationship between the \"step\" and \"horizon\" parameters. In the aggregate approach, a single forecast is produced for the entire period (e.g., total wind-generated energy over 24 h), and an error metric (such as RMSE or MAE) is applied to that aggregated value. As positive and negative deviations within the period can offset one another, this method often yields lower, potentially optimistic error estimates. In the sequential approach, the same horizon is divided into smaller intervals (e.g., 24 one-hour forecasts), each squared error is calculated, these nonnegative values are summed, and the appropriate root or average is taken. Key performance indicators, such as normalized mean absolute error and normalized root mean squared error, were extracted from the literature [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52] and visualized in The forecasting step of 1 h (sequential approach, see Table 1) is also associated with higher errors, suggesting that capturing fine-grained variations in wind power generation remains challenging. Analysis of forecasting methods' performance metrics (see comparison of wind power forecasting models in Appendix A, Table A1) shows significant differences between different approaches. Statistical methods work well for stable conditions but struggle with rapid wind changes. Machine learning models, such as XGBoost and neural networks, perform better by capturing complex patterns and reducing errors. However, even these models have limitations, especially for short-term (24-h) forecasting and fine time (1-h) steps, where errors remain high. Overall, analysis of the above studies indicates that hybrid forecasting approaches combining machine learning with dynamic time-series modeling are highly promising, with XGBoost and LTC models being the most promising ones. XGBoost is well-suited for selecting important features and handling structured data, but the model cannot track the changing behavior of the wind over time [21; 25]. LTC, on the other hand, is better at capturing and adjusting to wind fluctuations than standard neural networks [29]. Therefore, a study investigating how combining XGBoost and LTC strengths could improve the accuracy of wind power forecasts (especially short-term with a 1-h time step) seems to be meaningful and relevant.