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Considering the escalating rates of exhaustion of non-renewable energy resources, coupled with the harmful environmental side effects of harnessing them ( e.g.  damage to public health via air pollution), the need for a near-complete transition to renewable energy production seems inevitable. In recent times, renewable energy production has seen a strong support from investors, governmental initiatives, and industries across the world. Globally installed wind power capacity has seen an increase of 345.24% over the past decade. This increase brings along a need for robust power production management systems having a potential for predicting wind turbine power outputs primarily based on real-time input wind velocities. We propose and compare five optimized robust regression models for forecasting the wind power generated through turbines based on wind velocity vector components, out of which the Extreme Gradient Boosting regression model provided the best results. The forecasted output of our model can be compared with a city’s daily average threshold power requirement in order to make informed decisions about either shutting down an appropriate number of turbines to avoid excessive power production and wastage, or to compensate forecasted shortcomings in production on less windy days via alternative energy generation methodologies.

Comparative Assessment of Regression Techniques for Wind Power Forecasting