Quantifying the Influences on Probabilistic Wind Power Forecasts
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://doi.org/10.1051/e3sconf/20186406002 |
| _version_ | 1828967484392210432 |
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| author | Jens Schreiber Bernhard Sick |
| author_facet | Jens Schreiber Bernhard Sick |
| author_sort | Jens Schreiber |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T11:51:48Z |
| format | Article |
| id | doaj.art-18ea314234d2494ebba9f9240362ad26 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-14T11:51:48Z |
| publishDate | 2018-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-18ea314234d2494ebba9f9240362ad262022-12-21T23:02:17ZengEDP SciencesE3S Web of Conferences2267-12422018-01-01640600210.1051/e3sconf/20186406002e3sconf_icpre2018_06002Quantifying the Influences on Probabilistic Wind Power ForecastsJens SchreiberBernhard SickIn 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.https://doi.org/10.1051/e3sconf/20186406002 |
| spellingShingle | Jens Schreiber Bernhard Sick Quantifying the Influences on Probabilistic Wind Power Forecasts E3S Web of Conferences |
| title | Quantifying the Influences on Probabilistic Wind Power Forecasts |
| title_full | Quantifying the Influences on Probabilistic Wind Power Forecasts |
| title_fullStr | Quantifying the Influences on Probabilistic Wind Power Forecasts |
| title_full_unstemmed | Quantifying the Influences on Probabilistic Wind Power Forecasts |
| title_short | Quantifying the Influences on Probabilistic Wind Power Forecasts |
| title_sort | quantifying the influences on probabilistic wind power forecasts |
| url | https://doi.org/10.1051/e3sconf/20186406002 |
| work_keys_str_mv | AT jensschreiber quantifyingtheinfluencesonprobabilisticwindpowerforecasts AT bernhardsick quantifyingtheinfluencesonprobabilisticwindpowerforecasts |