# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 2 Existing Wind Power Forecasting Solutions and Their Limitations  
Solutions offered today for wind power prediction include the application of 4 different groups of methods: forecasting using physical methods, statistical time series methods, machine learning methods, and hybrid strategies. Based on insights from existing research, the following section of this publication describes strengths, weaknesses and typical forecasting horizons (short, medium or long term) for each group of methods, highlighting existing gaps and emphasizing the importance of new approaches that can lead to a more accurate and efficient wind farm power output forecast. Early work in wind power forecasting relied on physics-based models known as Numerical Weather Prediction (NWP), which solve atmospheric fluid-flow equations and use parameterizations to simulate wind fields in specific regions [1]. While these methods are scientifically reliable for medium- to long-range forecasts, they are computationally demanding and often less effective for short-term predictions involving turbulent or rapidly changing conditions [2; 3]. Attempts to improve their real-time performance by integrating NWP outputs into additional models, including those with lagged exogenous inputs, have shown limited success and depend heavily on local wind characteristics [4]. Statistical time-series methods, such as Auto-Regressive Moving Average (ARMA), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA), became popular due to their simplicity and ability to model temporal dependencies [5]. These models rely on autocorrelation and seasonality patterns, making them easy to implement under relatively stable conditions. However, they often struggle with capturingstrong non-linearities present in wind data, which can limit their prediction accuracy. Studies have shown that ARIMA models, when properly tuned, can achieve an accuracy improvement of around 10-15% over basic persistence models in stable wind conditions but tend to perform 20-30% worse than machine learning approaches in a highly fluctuating environment [6]. Their effectiveness also depends on stationarity assumptions, which can introduce compounding errors when actual conditions deviate from expected seasonal patterns. To address this, some researchers incorporate lag operators of measured or forecasted wind speeds to capture short-term temporal dynamics. While this approach improves short-term forecasts by 5-10% in some cases, it is still insufficient in more complex scenarios where rapid wind changes occur [6]. Overall, statistical methods remain useful for baseline forecasting but are increasingly being outperformed by more advanced machine learning techniques, particularly in non-stationary wind conditions. Machine learning (ML) methods started to expand as more wind data with high frequency and other meteorological information, like temperature, humidity, and pressure, became available [7; 8; 9]. Some known methods, like Support Vector Machines, K-Nearest Neighbors, Random Forests, and especially XGBoost, gain attention because they can learn complex relations and often use past data at different time gaps (for example, 5 to 35 min or several hours before prediction). These models often perform better than simple ones, partly because they automatically select important features and avoid overfitting [9]. Studies show that well-tuned XGBoost can reduce mean absolute error by about 20-27% compared to simple configurations, while still being quite fast to train [10; 11]. Deep neural networks (NNs) also play an important role in wind energy forecasting due to their ability to learn complex patterns over time and space [12]. Models like Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are made to work with sequences of data, making them good for handling fast-changing weather conditions [13; 14].