What question did this study set out to answer?

The aim is to enhance the accuracy of wind power forecasting through machine learning techniques.

June 12, 2026Open Access

Machine Learning Techniques For Accurate Active Wind Power Forecasting

Key Points

The aim is to enhance the accuracy of wind power forecasting through machine learning techniques.
Utilized multiple regression algorithms: K-Nearest Neighbors, Random Forest, Gradient Boosting, and Support Vector Regression.
Performed 5-fold cross-validation for model evaluation.
Conducted feature importance analysis to identify key parameters affecting power generation.
K-Nearest Neighbors achieved a Mean Absolute Error (MAE) of 51.61 kW, a Root Mean Square Error (RMSE) of 66.04 kW, and an R2 value of 0.9687.
The predictive models demonstrated robust performance, enhancing grid stability and integrating renewable energy resources.

Abstract

Abstract—The wind energy has also become an urgent elementof a sustainable power network, but the reliability of the accurate prediction of the power remains problematic because of the na-ture of variability of the wind conditions. This paper provides an exhaustive machine learning framework of active predictive windpower action plans on turbine operation and weather details. The multiple regression algorithms analyzed in this paper are K-Nearest Neighbors (KNN), Random Forest, Gradient Boosting, Support Vector Regression and cross-validation is done using5-fold. The feature importance analysis determines importantparameters that determine power generation. The experimentalresults show good predictive performance where KNN had MAEof 51.61 kW, RMSE of 66.04 kW and R2 of 0.9687. The gridhelps increase grid stability and integration of renewable energy.Terms—Wind power forecasting, machine learning,regression models, renewable energy, cross-validation, featureimportance, grid stability

Read Full Paperexternally

Mark Helpful

Bookmark

Relay

View Full Paper