Matches in SemOpenAlex for { <https://semopenalex.org/work/W2999761246> ?p ?o ?g. }
- W2999761246 endingPage "2025" @default.
- W2999761246 startingPage "2017" @default.
- W2999761246 abstract "With the increasing penetration of photovoltaic (PV) systems, the problems posed by the inherent intermittency of small-scale PVs are becoming more severe. To address this issue, it is critical to involve the uncertainty of PV generation in the look-ahead periods in a comprehensive framework. To this end, a direct deep learning architecture for probabilistic forecasting of solar generation is proposed in this paper. An end-to-end deep learning architecture as a novel mixture density network (MDN) is designed based on the combination of a convolutional neural network and a gated recurrent unit. Furthermore, a new loss function and training process based on adversarial training is proposed to enhance the accuracy in direct contracting of the probability density function. Then, several deep and shallow networks are implemented, and the results are compared with the proposed architecture. The effectiveness of the proposed MDN in providing complete statistical information is verified through comparison with Monte Carlo dropout, non-parametric kernel density estimation, and the proposed MDN without adversarial training." @default.
- W2999761246 created "2020-01-23" @default.
- W2999761246 creator A5012336081 @default.
- W2999761246 creator A5018295351 @default.
- W2999761246 creator A5040760270 @default.
- W2999761246 creator A5044201204 @default.
- W2999761246 date "2020-04-22" @default.
- W2999761246 modified "2023-10-06" @default.
- W2999761246 title "Deep learning architecture for direct probability density prediction of small‐scale solar generation" @default.
- W2999761246 cites W1120922828 @default.
- W2999761246 cites W2014454218 @default.
- W2999761246 cites W2313296464 @default.
- W2999761246 cites W2343702657 @default.
- W2999761246 cites W2519051700 @default.
- W2999761246 cites W2539201476 @default.
- W2999761246 cites W2548694017 @default.
- W2999761246 cites W2592036976 @default.
- W2999761246 cites W2736385547 @default.
- W2999761246 cites W2791648058 @default.
- W2999761246 cites W2796631030 @default.
- W2999761246 cites W2800410101 @default.
- W2999761246 cites W2807030657 @default.
- W2999761246 cites W2807966350 @default.
- W2999761246 cites W2834494841 @default.
- W2999761246 cites W2884484663 @default.
- W2999761246 cites W2888449021 @default.
- W2999761246 cites W2890704507 @default.
- W2999761246 cites W2891810618 @default.
- W2999761246 cites W2905035404 @default.
- W2999761246 cites W2906857449 @default.
- W2999761246 cites W2911027487 @default.
- W2999761246 cites W2912834121 @default.
- W2999761246 cites W2927060067 @default.
- W2999761246 cites W2940484810 @default.
- W2999761246 cites W2942641215 @default.
- W2999761246 cites W2953038294 @default.
- W2999761246 cites W2954616559 @default.
- W2999761246 cites W2955673578 @default.
- W2999761246 cites W2963410812 @default.
- W2999761246 cites W2963765772 @default.
- W2999761246 cites W2965206442 @default.
- W2999761246 cites W2965399094 @default.
- W2999761246 cites W2965516457 @default.
- W2999761246 cites W2965612495 @default.
- W2999761246 cites W2965939572 @default.
- W2999761246 cites W2975362660 @default.
- W2999761246 cites W2993533229 @default.
- W2999761246 cites W3119055825 @default.
- W2999761246 cites W4321994244 @default.
- W2999761246 cites W4376453727 @default.
- W2999761246 cites W4376596133 @default.
- W2999761246 cites W774145425 @default.
- W2999761246 cites W832636891 @default.
- W2999761246 doi "https://doi.org/10.1049/iet-gtd.2019.1289" @default.
- W2999761246 hasPublicationYear "2020" @default.
- W2999761246 type Work @default.
- W2999761246 sameAs 2999761246 @default.
- W2999761246 citedByCount "21" @default.
- W2999761246 countsByYear W29997612462020 @default.
- W2999761246 countsByYear W29997612462021 @default.
- W2999761246 countsByYear W29997612462022 @default.
- W2999761246 countsByYear W29997612462023 @default.
- W2999761246 crossrefType "journal-article" @default.
- W2999761246 hasAuthorship W2999761246A5012336081 @default.
- W2999761246 hasAuthorship W2999761246A5018295351 @default.
- W2999761246 hasAuthorship W2999761246A5040760270 @default.
- W2999761246 hasAuthorship W2999761246A5044201204 @default.
- W2999761246 hasConcept C105795698 @default.
- W2999761246 hasConcept C108583219 @default.
- W2999761246 hasConcept C117251300 @default.
- W2999761246 hasConcept C119599485 @default.
- W2999761246 hasConcept C119857082 @default.
- W2999761246 hasConcept C121332964 @default.
- W2999761246 hasConcept C127413603 @default.
- W2999761246 hasConcept C153294291 @default.
- W2999761246 hasConcept C154945302 @default.
- W2999761246 hasConcept C185429906 @default.
- W2999761246 hasConcept C189508267 @default.
- W2999761246 hasConcept C193415008 @default.
- W2999761246 hasConcept C196558001 @default.
- W2999761246 hasConcept C197055811 @default.
- W2999761246 hasConcept C2776145597 @default.
- W2999761246 hasConcept C2780388094 @default.
- W2999761246 hasConcept C33923547 @default.
- W2999761246 hasConcept C38652104 @default.
- W2999761246 hasConcept C41008148 @default.
- W2999761246 hasConcept C41291067 @default.
- W2999761246 hasConcept C49937458 @default.
- W2999761246 hasConcept C50644808 @default.
- W2999761246 hasConcept C71134354 @default.
- W2999761246 hasConcept C81363708 @default.
- W2999761246 hasConceptScore W2999761246C105795698 @default.
- W2999761246 hasConceptScore W2999761246C108583219 @default.
- W2999761246 hasConceptScore W2999761246C117251300 @default.
- W2999761246 hasConceptScore W2999761246C119599485 @default.
- W2999761246 hasConceptScore W2999761246C119857082 @default.
- W2999761246 hasConceptScore W2999761246C121332964 @default.
- W2999761246 hasConceptScore W2999761246C127413603 @default.