A methodology for optimizing probabilistic wind power forecasting
<p>Deterministic wind power forecasts enclose an inherent uncertainty due to several sources of error. In order to counterbalance this deficiency, an analysis of the error characteristics and construction of probabilistic forecasts with associated confidence levels is necessary for the qua...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-09-01
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| Series: | Advances in Geosciences |
| Online Access: | https://www.adv-geosci.net/45/289/2018/adgeo-45-289-2018.pdf |
| Summary: | <p>Deterministic wind power forecasts enclose an inherent uncertainty due to
several sources of error. In order to counterbalance this deficiency, an
analysis of the error characteristics and construction of probabilistic
forecasts with associated confidence levels is necessary for the
quantification of the corresponding uncertainty. This work proposes a
probabilistic forecasting method using an atmospheric model, optimization
techniques for addressing the temporal error dependencies and Kalman
filtering for eliminating systematic errors and enhancing the
symmetry-normality of the shaped error distributions. The method is applied
in case studies, using real time data from four wind farms in Greece. The
performance is compared against a reference method as well as other common
methods showing an improvement in the predictive reliability.</p> |
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| ISSN: | 1680-7340 1680-7359 |