Quantifying the Influences on Probabilistic Wind Power Forecasts
This work addresses the need to understand input influences for improving probabilistic wind power forecasts, which is crucial for integrating renewable energy into the electrical grid, but it is incremental as it applies existing sensitivity analysis techniques to this domain.
The study investigated how inputs like numerical weather predictions affect the output of probabilistic wind power forecasting models using sensitivity analysis on three black-box models, revealing that the number and type of influences vary with predicted probability and model type, which can guide model selection for practical applications.
In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid. These techniques allow decision makers to take the uncertainty of the prediction into account and, therefore, to devise optimal decisions, e.g., related to costs and risks in the electrical grid. However, it was yet not studied how the input, such as numerical weather predictions, affects the model output of forecasting models in detail. Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models. The analysis shows a considerable number of potential influences in those models depending on, e.g., the predicted probability and the type of model. These effects motivate the need to take various influences into account when models are tested, analyzed, or compared. Nevertheless, results of the sensitivity analysis will allow us to select a model with advantages in the practical application.