Performance Comparison of Different Machine Learning Algorithms on the Prediction of Wind Turbine Power Generation

TL;DR

This paper compares the prediction performance of linear regression, k-nearest neighbor, and decision tree algorithms for wind power forecasting, highlighting the importance of meteorological parameters like wind speed.

Contribution

It provides a detailed comparison of machine learning algorithms for wind power prediction and evaluates meteorological factors influencing accuracy.

Findings

Decision tree regression yields lower mean absolute error.

Wind speed is the most important meteorological parameter.

K-nearest neighbor regression has lower coefficient of determination.

Abstract

Over the past decade, wind energy has gained more attention in the world. However, owing to its indirectness and volatility properties, wind power penetration has increased the difficulty and complexity in dispatching and planning of electric power systems. Therefore, it is needed to make the high-precision wind power prediction in order to balance the electrical power. For this purpose, in this study, the prediction performance of linear regression, k-nearest neighbor regression and decision tree regression algorithms is compared in detail. k-nearest neighbor regression algorithm provides lower coefficient of determination values, while decision tree regression algorithm produces lower mean absolute error values. In addition, the meteorological parameters of wind speed, wind direction, barometric pressure and air temperature are evaluated in terms of their importance on the wind power parameter. The biggest importance factor is achieved by wind speed parameter. In consequence, many useful assessments are made for wind power predictions.