Quantifying the Influences on Probabilistic Wind Power Forecasts

by   Jens Schreiber, et al.

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.



There are no comments yet.


page 3


Reconciliation of probabilistic forecasts with an application to wind power

New methods are proposed for adjusting probabilistic forecasts to ensure...

Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models

Despite the increasing importance of forecasts of renewable energy, curr...

The application of sub-seasonal to seasonal (S2S) predictions for hydropower forecasting

Inflow forecasts play an essential role in the management of hydropower ...

Polyhedral Predictive Regions For Power System Applications

Despite substantial improvement in the development of forecasting approa...

Application of hypercomplex number system in the dynamic network model

In recent years, the direction of the study of networks in which connect...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.