Site-specific production forecast through data-driven and engineering models

The present study investigates the predictive capabilities of two site-specific wind power forecasting models. The two approaches make use of a machine learning ensemble model that combines forecasts from several numerical weather predictions (NWP) to match local observations. In the first approach, the training data consists of historical site power production recordings. This yields a black-box model that includes the wind farm behaviour. The second approach is a combination of data-driven and explicit modelling. In the first step, the machine learning ensemble model forecasts the site wind conditions. Then, this input is fed to a site-specific engineering wake model. This hybrid "grey-box"approach explicitly resolves some farm flow effects. The study shows that the performance of both methods differs over the wind speed range. The purely data-driven model performs better at medium wind speeds, which also occur more often. The hybrid method predictions agree better during periods of higher wind speeds. In the last part, the study showcases the capabilities of the forecasting methods when participating in the energy market. Additionally, the engineering wake model allows for power maximisation through wind farm control.