A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

In the last few years, several countries have accomplished their determined renewable energy targets to achieve future requirements with foremost aim encourage sustainable growth reduced emissions, mainly through implementation of wind and solar energy. present study, we propose compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting (GBM), k-nearest neighbor (kNN), decision-tree, extra tree regression, which are applied improve forecasting accuracy short-term generation in Turkish farms, situated west Turkey, on basis a historic data speed direction. Polar diagrams plotted impacts input variables such as direction examined. Scatter curves depicting relationships between produced turbine power for all methods predicted average is compared real from help error curves. The results demonstrate superior performance algorithm incorporating regression.