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High frequency forecasting for wind energy using statistical and machine learning post-processing methods

With the increase in wind energy production being fed into the power grid accurate high frequency predictions of the estimate d power for the next hours and days ahead are needed to schedule feed-in rates and secure power grid stability. To achieve this a combination of different kinds of information and data sets are needed. Here, statistical and machine learning methods proved to be a suitable tool. However, a thorough selection of input data is needed as well as considering extreme events (upper and lower tails) in model training and avoiding smoothed forecasts.

A brief introduction into post-processing for wind energy applications using statistics and machine learning, including useful tools/methods/data, will be given.

 Lecture slides...