Predicting wind power generation using Light_Gradient_Boosting_Machine andCNN-LSTM approaches

Energy production using wind turbines will replace fossil fuel power plants because access to fossil fuels is hardly possible in all countries. Their use causes environmental degradation and disease in humans and other living organisms. The accessible nature of wind energy and its availability in all parts of the world We decided to predict the production capacity of wind turbines, so we intend to use two algorithms of light gradient booster machine and short-term memory to predict the production capacity of wind turbines and the results obtained from these two The method is compared with each other. Using the light gradient amplification machine method, we achieved an average error of 5.12 in 45 seconds and 4.77 in 450 seconds using the short-term memory method.

Predicting wind power generation using Light_Gradient_Boosting_Machine andCNN-LSTM approaches Keywords:

Wind turbine , turbine production capacity , light gradient boosting machine , CNN-LSTM method