FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048169169> ?p ?o ?g. }

• W3048169169 endingPage "5943" @default.
• W3048169169 startingPage "5935" @default.
• W3048169169 abstract "The increasing penetration of renewable energy sources causes complex uncertainties of the power system. To capture such uncertainties in power system planning, an important step is to generate representative scenarios. In this work, a long short term memory (LSTM) auto-encoder based approach is proposed to generate representative scenarios in an integrated hydro-photovoltaic (PV) power generation system, which consists of feature extraction by LSTM Encoder, scenario clustering in feature domain by combining gap statistics method and K-means++, and representative scenario reconstruction by using LSTM Decoder. Compared with traditional scenario selection and generation methods, the proposed method can better capture the patterns of multivariate time-series data in both temporal and spatial dimensions. A case study in southwest China is used to demonstrate the effectiveness of the proposed method, which outperforms other existing methods by achieving the lowest SSE and DBI indices of 0.89 and 0.12, respectively, and obtaining the best SIL and CHI scores of 0.93 and 2.30, respectively, In addition, the case study shows the proposed model setup works more stable for scenario generation." @default.
• W3048169169 created "2020-08-13" @default.
• W3048169169 creator A5007322337 @default.
• W3048169169 creator A5017102282 @default.
• W3048169169 creator A5031045561 @default.
• W3048169169 creator A5043956394 @default.
• W3048169169 creator A5044235056 @default.
• W3048169169 creator A5044611363 @default.
• W3048169169 creator A5079614923 @default.
• W3048169169 date "2020-08-04" @default.
• W3048169169 modified "2023-10-10" @default.
• W3048169169 title "LSTM auto‐encoder based representative scenario generation method for hybrid hydro‐PV power system" @default.
• W3048169169 cites W1980287119 @default.
• W3048169169 cites W1995331911 @default.
• W3048169169 cites W2009012905 @default.
• W3048169169 cites W2036218954 @default.
• W3048169169 cites W2059986660 @default.
• W3048169169 cites W2071949631 @default.
• W3048169169 cites W2079309933 @default.
• W3048169169 cites W2097535806 @default.
• W3048169169 cites W2100495367 @default.
• W3048169169 cites W2106007600 @default.
• W3048169169 cites W2113810680 @default.
• W3048169169 cites W2114516216 @default.
• W3048169169 cites W2120512785 @default.
• W3048169169 cites W2149420896 @default.
• W3048169169 cites W2328146686 @default.
• W3048169169 cites W2342486629 @default.
• W3048169169 cites W2577790064 @default.
• W3048169169 cites W2587549331 @default.
• W3048169169 cites W2596479026 @default.
• W3048169169 cites W2608920339 @default.
• W3048169169 cites W2638027694 @default.
• W3048169169 cites W2739824434 @default.
• W3048169169 cites W2754825658 @default.
• W3048169169 cites W2763583057 @default.
• W3048169169 cites W2771792916 @default.
• W3048169169 cites W2789548662 @default.
• W3048169169 cites W2801354104 @default.
• W3048169169 cites W2801723110 @default.
• W3048169169 cites W2803280656 @default.
• W3048169169 cites W2803620531 @default.
• W3048169169 cites W2806806521 @default.
• W3048169169 cites W2807764341 @default.
• W3048169169 cites W2822721003 @default.
• W3048169169 cites W2906824711 @default.
• W3048169169 cites W2908818658 @default.
• W3048169169 cites W2910148935 @default.
• W3048169169 cites W2914532067 @default.
• W3048169169 cites W2944588927 @default.
• W3048169169 cites W2953562924 @default.
• W3048169169 cites W2969775576 @default.
• W3048169169 cites W2970261355 @default.
• W3048169169 cites W2974980961 @default.
• W3048169169 cites W3014672219 @default.
• W3048169169 cites W3015999613 @default.
• W3048169169 doi "https://doi.org/10.1049/iet-gtd.2020.0757" @default.
• W3048169169 hasPublicationYear "2020" @default.
• W3048169169 type Work @default.
• W3048169169 sameAs 3048169169 @default.
• W3048169169 citedByCount "16" @default.
• W3048169169 countsByYear W30481691692020 @default.
• W3048169169 countsByYear W30481691692021 @default.
• W3048169169 countsByYear W30481691692022 @default.
• W3048169169 countsByYear W30481691692023 @default.
• W3048169169 crossrefType "journal-article" @default.
• W3048169169 hasAuthorship W3048169169A5007322337 @default.
• W3048169169 hasAuthorship W3048169169A5017102282 @default.
• W3048169169 hasAuthorship W3048169169A5031045561 @default.
• W3048169169 hasAuthorship W3048169169A5043956394 @default.
• W3048169169 hasAuthorship W3048169169A5044235056 @default.
• W3048169169 hasAuthorship W3048169169A5044611363 @default.
• W3048169169 hasAuthorship W3048169169A5079614923 @default.
• W3048169169 hasBestOaLocation W30481691691 @default.
• W3048169169 hasConcept C111919701 @default.
• W3048169169 hasConcept C118505674 @default.
• W3048169169 hasConcept C119599485 @default.
• W3048169169 hasConcept C121332964 @default.
• W3048169169 hasConcept C127413603 @default.
• W3048169169 hasConcept C133731056 @default.
• W3048169169 hasConcept C163258240 @default.
• W3048169169 hasConcept C171146098 @default.
• W3048169169 hasConcept C41008148 @default.
• W3048169169 hasConcept C41291067 @default.
• W3048169169 hasConcept C62520636 @default.
• W3048169169 hasConceptScore W3048169169C111919701 @default.
• W3048169169 hasConceptScore W3048169169C118505674 @default.
• W3048169169 hasConceptScore W3048169169C119599485 @default.
• W3048169169 hasConceptScore W3048169169C121332964 @default.
• W3048169169 hasConceptScore W3048169169C127413603 @default.
• W3048169169 hasConceptScore W3048169169C133731056 @default.
• W3048169169 hasConceptScore W3048169169C163258240 @default.
• W3048169169 hasConceptScore W3048169169C171146098 @default.
• W3048169169 hasConceptScore W3048169169C41008148 @default.
• W3048169169 hasConceptScore W3048169169C41291067 @default.
• W3048169169 hasConceptScore W3048169169C62520636 @default.
• W3048169169 hasFunder F4320335777 @default.
• W3048169169 hasIssue "24" @default.

• next