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Wind power has grown significantly over the last decade regarding its combability with emission targets and climate change in many countries. A reliable and accurate approach to wind power forecasting is critical for power system operations and day-to-day grid functioning. However, regarding to the nonstationary nature of wind power series, classic forecasting methods can hardly provide the desired accuracy and cause risks and uncertainties for system operation, which substantially affects how wind power companies make energy market decisions. This study proposes novel algorithmic approaches utilizing machine learning techniques to predict wind turbine power. Applied algorithms include extremely randomized trees, light gradient boosting machine, ensemble methods, and the CNN-LSTM method. Based on the provided results, the lowest mean square error value is related to the CNN-LSTM method, indicating that this method is more accurate. Also, the ensemble method provides admissible results despite the high speed of the algorithm.

Recently, power systems have faced the challenges of growing electricity demand, reducing fossil fuels, and exacerbating environmental pollution due to carbon emissions from fossil fuel-based power generation. Integrating low-carbon alternative energy, renewable energy sources (RES), is becoming very important for energy systems. Effective management of the integration of the production capacity of RES is as important as the production capacity of wind farms with the production capacity of fossil fuel power plants. This article analyzed 850,660 data recorded by a wind farm from March 01, 2020, 00:00:00 to December 31, t2020, 23:50:00 were analyzed. And by using machine learning and extra tree, light gradient boosting machine, gradient boosting regressor, decision tree, Ada Boost, and ridge algorithms, the production power of the wind farm was predicted. The best performance predicting the turbine production power was assigned to extra tree, and the worst performance was related to the Ridge algorithm.

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