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A methodology for optimizing probabilistic wind power forecasting
- Source :
- Advances in Geosciences, Vol 45, Pp 289-294 (2018)
- Publication Year :
- 2018
-
Abstract
- 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.
- Subjects :
- Mathematical optimization
010504 meteorology & atmospheric sciences
lcsh:Dynamic and structural geology
Computer science
020209 energy
Wind power forecasting
02 engineering and technology
Atmospheric model
01 natural sciences
7. Clean energy
lcsh:QE500-639.5
0202 electrical engineering, electronic engineering, information engineering
Real-time data
lcsh:Science
Reliability (statistics)
Physics::Atmospheric and Oceanic Physics
0105 earth and related environmental sciences
Wind power
business.industry
lcsh:QE1-996.5
Probabilistic logic
General Medicine
Kalman filter
lcsh:Geology
lcsh:Q
Probabilistic forecasting
business
Subjects
Details
- Language :
- English
- ISSN :
- 16807359
- Database :
- OpenAIRE
- Journal :
- Advances in Geosciences, Vol 45, Pp 289-294 (2018)
- Accession number :
- edsair.doi.dedup.....18cdc11ad781125e9d8bc9aecbec5962