Matches in SemOpenAlex for { <https://semopenalex.org/work/W2548694017> ?p ?o ?g. }
- W2548694017 endingPage "2778" @default.
- W2548694017 startingPage "2767" @default.
- W2548694017 abstract "The fluctuation and uncertainty of wind power generation bring severe challenges to secure and economic operation of power systems. Because wind power forecasting error is unavoidable, probabilistic forecasting becomes critical to accurately quantifying the uncertainty involved in traditional point forecasts of wind power and to providing meaningful information to conduct risk management in power system operation. This paper proposes a novel direct quantile regression approach to efficiently generate nonparametric probabilistic forecasting of wind power generation combining extreme learning machine and quantile regression. Quantiles with different proportions can be directly produced via an innovatively formulated linear programming optimization model, without dependency on point forecasts. Multistep probabilistic forecasting of 10-min wind power is newly carried out based on real wind farm data from Bornholm Island in Denmark. The superiority of the proposed approach is verified through comparisons with other well-established benchmarks. The proposed approach forms a new artificial neural network-based nonparametric forecasting framework for wind power with high efficiency, reliability, and flexibility, which can be beneficial to various decision-making activities in power systems." @default.
- W2548694017 created "2016-11-11" @default.
- W2548694017 creator A5001527824 @default.
- W2548694017 creator A5011279096 @default.
- W2548694017 creator A5030446424 @default.
- W2548694017 creator A5064691727 @default.
- W2548694017 creator A5069906308 @default.
- W2548694017 date "2017-07-01" @default.
- W2548694017 modified "2023-10-15" @default.
- W2548694017 title "Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation" @default.
- W2548694017 cites W1541373540 @default.
- W2548694017 cites W1864372840 @default.
- W2548694017 cites W1966161122 @default.
- W2548694017 cites W1966818217 @default.
- W2548694017 cites W1967160281 @default.
- W2548694017 cites W1982399266 @default.
- W2548694017 cites W1991181079 @default.
- W2548694017 cites W2002231816 @default.
- W2548694017 cites W2003065554 @default.
- W2548694017 cites W2006558836 @default.
- W2548694017 cites W2017108743 @default.
- W2548694017 cites W2026131661 @default.
- W2548694017 cites W2034544282 @default.
- W2548694017 cites W2040298412 @default.
- W2548694017 cites W2054458966 @default.
- W2548694017 cites W2086691266 @default.
- W2548694017 cites W2096904991 @default.
- W2548694017 cites W2101457442 @default.
- W2548694017 cites W2109762852 @default.
- W2548694017 cites W2111051539 @default.
- W2548694017 cites W2112159553 @default.
- W2548694017 cites W2114471530 @default.
- W2548694017 cites W2116224582 @default.
- W2548694017 cites W2118788550 @default.
- W2548694017 cites W2125336244 @default.
- W2548694017 cites W2132634326 @default.
- W2548694017 cites W2137983211 @default.
- W2548694017 cites W2141838814 @default.
- W2548694017 cites W2145334893 @default.
- W2548694017 cites W2153520025 @default.
- W2548694017 cites W2156604062 @default.
- W2548694017 cites W2157110120 @default.
- W2548694017 cites W2158054309 @default.
- W2548694017 cites W2158525236 @default.
- W2548694017 cites W2163707786 @default.
- W2548694017 cites W2165799067 @default.
- W2548694017 cites W2175195964 @default.
- W2548694017 cites W2322676867 @default.
- W2548694017 cites W2343702657 @default.
- W2548694017 cites W2515948533 @default.
- W2548694017 cites W2546467361 @default.
- W2548694017 cites W2566493587 @default.
- W2548694017 cites W4241653265 @default.
- W2548694017 cites W4291236916 @default.
- W2548694017 doi "https://doi.org/10.1109/tpwrs.2016.2625101" @default.
- W2548694017 hasPublicationYear "2017" @default.
- W2548694017 type Work @default.
- W2548694017 sameAs 2548694017 @default.
- W2548694017 citedByCount "182" @default.
- W2548694017 countsByYear W25486940172017 @default.
- W2548694017 countsByYear W25486940172018 @default.
- W2548694017 countsByYear W25486940172019 @default.
- W2548694017 countsByYear W25486940172020 @default.
- W2548694017 countsByYear W25486940172021 @default.
- W2548694017 countsByYear W25486940172022 @default.
- W2548694017 countsByYear W25486940172023 @default.
- W2548694017 crossrefType "journal-article" @default.
- W2548694017 hasAuthorship W2548694017A5001527824 @default.
- W2548694017 hasAuthorship W2548694017A5011279096 @default.
- W2548694017 hasAuthorship W2548694017A5030446424 @default.
- W2548694017 hasAuthorship W2548694017A5064691727 @default.
- W2548694017 hasAuthorship W2548694017A5069906308 @default.
- W2548694017 hasBestOaLocation W25486940171 @default.
- W2548694017 hasConcept C102366305 @default.
- W2548694017 hasConcept C118671147 @default.
- W2548694017 hasConcept C119599485 @default.
- W2548694017 hasConcept C119857082 @default.
- W2548694017 hasConcept C121332964 @default.
- W2548694017 hasConcept C122282355 @default.
- W2548694017 hasConcept C126255220 @default.
- W2548694017 hasConcept C127413603 @default.
- W2548694017 hasConcept C149782125 @default.
- W2548694017 hasConcept C154945302 @default.
- W2548694017 hasConcept C163258240 @default.
- W2548694017 hasConcept C2781084341 @default.
- W2548694017 hasConcept C33923547 @default.
- W2548694017 hasConcept C41008148 @default.
- W2548694017 hasConcept C49937458 @default.
- W2548694017 hasConcept C50644808 @default.
- W2548694017 hasConcept C62520636 @default.
- W2548694017 hasConcept C63817138 @default.
- W2548694017 hasConcept C78600449 @default.
- W2548694017 hasConcept C89227174 @default.
- W2548694017 hasConceptScore W2548694017C102366305 @default.
- W2548694017 hasConceptScore W2548694017C118671147 @default.
- W2548694017 hasConceptScore W2548694017C119599485 @default.
- W2548694017 hasConceptScore W2548694017C119857082 @default.
- W2548694017 hasConceptScore W2548694017C121332964 @default.