# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 2 Existing Wind Power Forecasting Solutions and Their Limitations
However, the decision on the importance of historical data is not always straightforward, and often the training of the model requires the provision of key historical data (lags) to help the model predict better [15]. This way of setting up data helps the model focus on important moments such as sudden changes in wind speed or temperature fluctuations [16]. More recently, transformer models have been explored for time series forecasting tasks due to their ability to capture long-range dependencies using self-attention mechanisms rather than recurrence [17]. Transformers offer improved scalability and parallelization during training, but they may require more data and computational resources, and can be sensitive to hyperparameter choices [18]. Researchers often find that deep learning models perform 15% to 25% better than traditional statistical methods [12; 14]. However, these improvements require longer training time and may need more powerful hardware for large experiments [13; 16]. Recent efforts to develop more flexible and understandable models have explored ideas inspired by biology. Neural Circuit Policies (NCPs) offer transparency at the neuron level, making them useful for energy system control [15; 19]. Liquid Time-constant Networks (LTCs) build on this by allowing neurons to adjust their timing dynamically, making them suitable for continuous-time modeling [20]. While LTC-based models can track fast-changing signals better than fixed architectures, they need careful handling of past data to prevent the accumulation of errors, and can be costly to run for large-scale wind forecasting [21]. They seem promising for short-term prediction, but clear performance benchmarks are still lacking, especially when real-world data is delayed or noisy [22]. A growing trend in wind energy forecasting involves the use of hybrid models combining physical simulations, statistical methods, and AI/ML models. In some studies, Convolutional Neural Networks (CNNs) have been combined with LSTM layers and attention mechanisms, which help to capture spatial patterns while keeping track of time-basedsequences and figuring out the most important inputs for forecasting [23]. In these cases, past data from Supervisory Control and Data Acquisition (SCADA) systems, like wind speed, direction, temperature, air pressure, and sometimes previous power outputs, are usually stacked as input layers with different time gaps. This kind of setup often leads to much more accurate predictions than using a single model. For example, one study on offshore wind forecasting used wavelet transforms, LSTM networks, and boosting algorithms, making RMSE better by more than 10% compared to Stand-alone SARIMA, LSTM, and Two-stage DWT + LSTM methods [24]. Another approach used a two-step model that mixed physical downscaling of weather forecasts with a deep ensemble, leading to more stable short-term forecasts [25]. One more hybrid method, which combined empirical mode decomposition with extreme gradient boosting, managed to get mean absolute percentage errors below 5% for predictions one hour ahead [24]. Even though these systems often take a lot of time to adjust and need expert knowledge, they show that combining different techniques is becoming more and more important for dealing with the wind's unpredictable nature. Researchers working to improve offshore wind power forecasting have developed hybrid models that combine wavelet decomposition with long short-term memory networks and introduce key innovations. Variational mode decomposition separates wind power signals into three physically meaningful components, including long-term trend, fluctuation, and randomness. The memory gates of the long short-term memory networks enable learning of long-term patterns for accurate multi-step forecasting [26]. Some approaches use metaheuristic optimization to fine-tune the balance between physically simulated wind-speed data and machine learning-based adjustments [27].