A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting

CC BYLuka IvanovićSaša MilićŽivko SokolovićAleksandar Rakić2025-06-192025-06-192024-01-010350-85282406-121210.5937/zeint34-51258https://ror.circle-u.eu/handle/123456789/1341917A vital step toward a sustainable future is the power grid's incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact. The paper presents a comparative analysis of recurrent neural network algorithms and gradient boosting machines applied to time series data for the regression issue of estimating the active power generated by a wind farm. Gradient boosting algorithms combine the advantages of a few machine learning models (decision trees, random forests, etc.) to produce a powerful prediction model. In addition to conventional recurrent neural networks, the article deals with long short-term memory and gated recurrent unit as cutting-edge models for time series analysis and predictions. A comprehensive analysis was carried out on a large wind power generation data set.OPENgradient boosting machinesxgboostmachine learningpower generationgated recurrent unitwind farmrecurrent neural networkElectrical engineering. Electronics. Nuclear engineeringlong short-term memoryTK1-9971A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecastingpublicationdoi_dedup___:fb36029ead67c37043a6d3166b8f5650