Efficient Forecasting Of Wind Power Using Machine Learning Methods.

Abstract

Efficient forecasting of wind power using machine learning methods

I. Introduction Wind power, one of the most promising green energies, has been developed rapidly globally, and its pro- portion in the power grid has been increasing. The main crucial and challenging issue in wind power production is its intermittent volatility due to weather conditions, making it not easy to integrate into the power grid. Precise forecasting of wind power generation is crucial to mitigate the challenges of balancing supply and demand in the smart grid. This study investigates the feasibility of different machine Learning to predict and forecast wind power. Actual measurements recorded every 10 minutes from three actual wind turbines are used to demonstrate the prediction precision of the investigated techniques.

II.Methodology Datasets were divided into two subsets: a training set and a testing set. We train the investigated machine learning models using the training set. We used a 5-fold cross-validation technique to train the models. 23 machine learning methods are investigated to forecast and predict wind power, including SVR, GPR, Bagged trees, Boosted trees, and Random forest. Bayesian optimization is employed to determine the values of the hyperparameters in the considered models

III. Datasets

Three wind power datasets are used in this study: France, Turkey, and Kaggle datasets. France Dataset contains 16 features collected in 2017: 21524 train records and 433 test records. Turkey Dataset comprises two features, wind speed, and wind direction, containing 21495 train records and 433 test records collected in 2018. Kaggle Dataset contains 20 features, 12333 train records, and 451 test records collected in 2021.

VI. Conclusion

Results indicate the promising performance of the ensemble Trees-based models We plan to apply deep learning methods to increase the prediction accuracy The investigated models in this study can represent a helpful tool for model-based anomaly detection in wind turbines.