Performance Comparison of Different Machine Learning Algorithms on the Prediction of Wind Turbine Power Generation
This work addresses the need for high-precision wind power prediction to balance electrical power systems, but it is incremental as it compares existing methods on a specific dataset.
The study compared linear regression, k-nearest neighbor regression, and decision tree regression algorithms for predicting wind turbine power generation, finding that decision tree regression produced lower mean absolute error values and wind speed was the most important meteorological parameter.
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.