ASSESMENT OF WIND POWER FORECASTING ERROR FOR GOTLAND

Abstract: When the wind blows and wind turbine generators harvests the kinetic energy and trans- forms it to electrical power, there is a need for predicting how much power that will be dispatched from the turbines. Even the most perfect computer model with high computa- tional power could not model the beauty of the forces of nature and we must accept some degree of forecasting error in the predicted power output due to the inherently stochastic patterns in the atmosphere.  This project set out to investigate the main reasons and factors that impacts the forecasting error related to wind power assets on Gotland. From theory and the performed case study, wind speed is the strongest predictor of wind power production, to claim anything else would be severely inaccurate. However, the main predictors of wind power prediction are summarized from a literature study, extracted from a weather model and tried in a case study for the wind farm Stugylparken on Näsudden, Gotland. Three different prediction methods were tried and the ensemble trees model was the best model by the evaluation metrics that was chosen. The second-best performing model was the artificial neural network, and prediction by theoretical power curve performed worse than the standard machine learning methods what was tested in the study. It can be noted that when assessing what model to choose, it depends on how the evaluation is done and which metric is deemed most important. Besides that wind speed will have the most significant impact in all models, forecasting error seem to have correlation to the diurnal cycle. One reason could be land-sea interaction during the day, especially at the period April-September. Higher forecasting errors correlates strongly to periods of a higher mean wind speed and times of varying weather will impact the forecastability and larger errors should be expected. In this project, numerical weather prediction data is used to investigate the forecasting error. A lower error can be seen at the first hours from the model run. This should be expected because it is when we are closest to the initial conditions, in other words, the real world. However, it seems like wind speed and diurnal cycle are more significant than the performance of the numerical weather prediction model in the first 24 hours.  Predicting the future power output of wind assets is expected to be even more impor- tant in the future years due to larger installed capacity. Even with an increase in installed capacity, an over capacity is not wanted and flexibility will be more important. There are challenges, but also opportunity to have a more efficient use of resources in our society and lowering the climate impact that our society has on the planet through a more flexible use of resources.