About this document

Wind Power Forecasting with LightGBM by Priyanka Kilaniya is a document available to read on EtoBox.

This study evaluates various machine learning models for short-term wind power forecasting, emphasizing the performance of LightGBM, which achieved a normalized mean squared error of 4.36%. The methodology includes data collection, correlation analysis, outlier removal, and dimensionality reduction, ultimately demonstrating that LightGBM outperforms traditional models like linear regression. The findings suggest that improved forecasting accuracy can enhance predictive maintenance planning for wind turbines

Author

Priyanka Kilaniya

Language

EN