# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
###### Abstract  
This publication presents a novel approach to predicting the amount of electricity generated by wind power plants. The research focuses on data-driven models such as XGBoost, Liquid Time-constant Networks, and covers both the analysis of properties of individual forecasting models as well as aspects of their integration into a hybrid model. By analyzing real-world weather scenarios, the approach aims to identify the highest accuracy forecasting model for the short-term 24-h forecast of wind farm power output. A more accurate forecast allows for more efficient resource planning and better distribution of resources on the electricity grids, thus ensuring a greener approach to energy production. The study shows that the proposed Hybrid (XGBoost + LTC) model predicts wind power generation with an nMAE of 0.0856, representing an improvement over standalone XGBoost and LTC models, and outperforming classical approaches such as LSTM and statistical models like ARIMAX in terms of forecasting accuracy.  
wind power prediction; hybrid model; XGBoost; LTC  
5 June 2005527 June  
the share of wind power in the national grid increases, so does the system's exposure to its variability and intermittency. Unlike conventional power plants, wind turbines cannot be dispatched on demand, and their output is subject to rapid fluctuations in meteorological conditions. Additionally, electricity generated from wind cannot be stored directly in large quantities using cost-effective means [8; 9; 10], which complicates integration into real-time electricity markets and power system operations.  
Accurate short-term forecasting of wind power--particularly within a 24-h horizon--is therefore vital for maintaining grid stability, minimizing imbalance penalties, optimizing market bids, and ensuring the efficient use of resources. This is especially relevant in markets such as Nord Pool, where energy producers are expected to submit precise day-ahead production forecasts. Improved forecasts can reduce the need for expensive reserves, prevent curtailments, and support decision-making in both grid operation and energy trading.  
To address these challenges, this research aims to create a data analysis and prediction model that could produce accurate short-term predictions of wind farm power output for the next 24 h. Analysis focuses on the development and evaluation of data-driven wind power forecasting models, combining recent advances in machine learning and neural network architectures. For prognosis, data on weather conditions, such as wind speed and temperature, will be used. By leveraging historical operational data and weather forecasts, and by benchmarking multiple modeling strategies, this work aims to enhance the reliability and economic performance of wind energy systems in Lithuania and beyond.  
The remainder of this paper is structured as follows. Section 2 provides a comprehensive review of existing wind forecasting studies, emphasizing the methodologies applied, their respective strengths and limitations, as well as assumptions that support the relevance of the authors' research. Section 3 details characteristics of the dataset employed in this study. Section 4 outlines the research methodology, while Section 5 presents an in-depth analysis of experimental results. Finally, Section 6 concludes the paper by summarizing the key findings, addressing identified limitations, and outlining potential directions for future research.