Matches in SemOpenAlex for { <https://semopenalex.org/work/W3199342842> ?p ?o ?g. }
- W3199342842 abstract "With more locations for wind generation, the grid's dependability is degraded. This paper presents a state-of-art combined wind power prediction model, including data preprocessing, improved secondary decomposition, and deep learning. A density-based spatial clustering of applications with noise was used primarily to identify and address irrational data and then correct them using k-nearest neighbor. Later, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the original wind power time series into several intrinsic mode functions (IMFs), and the variational mode decomposition (VMD) was adopted for further decomposition, due to its high irregularity and instability, of the first two components. Finally, a long short-term memory (LSTM) was employed to predict each component. The proposed model was then applied to two wind farms in Turkey and France. The experimental findings are as follows: (1) The data preprocessing scheme proposed in this paper can improve the predicted results. After data preprocessing, mean absolute error (MAE) and root mean squared error (RMSE) have declined by 10.73% and 10.20% on average, respectively. (2) The improved predictions were greater than the common secondary decomposition. The MAE and RMSE of improved CEEMDAN-VMD-LSTM were down by 14.77% and 15.12% on average, compared with CEEMDAN-VMD-LSTM, respectively." @default.
- W3199342842 created "2021-09-27" @default.
- W3199342842 creator A5000520484 @default.
- W3199342842 creator A5013529068 @default.
- W3199342842 creator A5021708893 @default.
- W3199342842 creator A5022563816 @default.
- W3199342842 creator A5028924208 @default.
- W3199342842 creator A5031444979 @default.
- W3199342842 creator A5044301848 @default.
- W3199342842 creator A5048416925 @default.
- W3199342842 creator A5078390477 @default.
- W3199342842 date "2021-09-01" @default.
- W3199342842 modified "2023-09-26" @default.
- W3199342842 title "Short-term wind power prediction based on preprocessing and improved secondary decomposition" @default.
- W3199342842 cites W2000982976 @default.
- W3199342842 cites W2007221293 @default.
- W3199342842 cites W2102836090 @default.
- W3199342842 cites W2113522414 @default.
- W3199342842 cites W2120139765 @default.
- W3199342842 cites W2122111042 @default.
- W3199342842 cites W2318232992 @default.
- W3199342842 cites W2321536237 @default.
- W3199342842 cites W23993467 @default.
- W3199342842 cites W2561807722 @default.
- W3199342842 cites W2600292797 @default.
- W3199342842 cites W2605620338 @default.
- W3199342842 cites W2742197121 @default.
- W3199342842 cites W2767506022 @default.
- W3199342842 cites W2775425027 @default.
- W3199342842 cites W2781009421 @default.
- W3199342842 cites W2791370308 @default.
- W3199342842 cites W2794466895 @default.
- W3199342842 cites W2810444707 @default.
- W3199342842 cites W2883904934 @default.
- W3199342842 cites W2893815961 @default.
- W3199342842 cites W2894229023 @default.
- W3199342842 cites W2910279921 @default.
- W3199342842 cites W2925057617 @default.
- W3199342842 cites W2942406509 @default.
- W3199342842 cites W2947870382 @default.
- W3199342842 cites W2965142511 @default.
- W3199342842 cites W2978016765 @default.
- W3199342842 cites W2979695010 @default.
- W3199342842 cites W2980223420 @default.
- W3199342842 cites W2983772465 @default.
- W3199342842 cites W2984148598 @default.
- W3199342842 cites W2984455290 @default.
- W3199342842 cites W2990255447 @default.
- W3199342842 cites W3006315601 @default.
- W3199342842 cites W3008244228 @default.
- W3199342842 cites W3009884378 @default.
- W3199342842 cites W3021035335 @default.
- W3199342842 cites W3036688307 @default.
- W3199342842 cites W3037778318 @default.
- W3199342842 cites W3039070036 @default.
- W3199342842 cites W3044165600 @default.
- W3199342842 cites W3082737753 @default.
- W3199342842 cites W3083758632 @default.
- W3199342842 cites W3085107006 @default.
- W3199342842 cites W3087568108 @default.
- W3199342842 cites W3091450960 @default.
- W3199342842 cites W3153473344 @default.
- W3199342842 cites W3159458251 @default.
- W3199342842 doi "https://doi.org/10.1063/5.0059809" @default.
- W3199342842 hasPublicationYear "2021" @default.
- W3199342842 type Work @default.
- W3199342842 sameAs 3199342842 @default.
- W3199342842 citedByCount "5" @default.
- W3199342842 countsByYear W31993428422022 @default.
- W3199342842 countsByYear W31993428422023 @default.
- W3199342842 crossrefType "journal-article" @default.
- W3199342842 hasAuthorship W3199342842A5000520484 @default.
- W3199342842 hasAuthorship W3199342842A5013529068 @default.
- W3199342842 hasAuthorship W3199342842A5021708893 @default.
- W3199342842 hasAuthorship W3199342842A5022563816 @default.
- W3199342842 hasAuthorship W3199342842A5028924208 @default.
- W3199342842 hasAuthorship W3199342842A5031444979 @default.
- W3199342842 hasAuthorship W3199342842A5044301848 @default.
- W3199342842 hasAuthorship W3199342842A5048416925 @default.
- W3199342842 hasAuthorship W3199342842A5078390477 @default.
- W3199342842 hasConcept C10551718 @default.
- W3199342842 hasConcept C105795698 @default.
- W3199342842 hasConcept C112633086 @default.
- W3199342842 hasConcept C11413529 @default.
- W3199342842 hasConcept C115961682 @default.
- W3199342842 hasConcept C119599485 @default.
- W3199342842 hasConcept C127413603 @default.
- W3199342842 hasConcept C139945424 @default.
- W3199342842 hasConcept C153180895 @default.
- W3199342842 hasConcept C154945302 @default.
- W3199342842 hasConcept C25570617 @default.
- W3199342842 hasConcept C33923547 @default.
- W3199342842 hasConcept C34736171 @default.
- W3199342842 hasConcept C41008148 @default.
- W3199342842 hasConcept C73555534 @default.
- W3199342842 hasConcept C78600449 @default.
- W3199342842 hasConcept C99498987 @default.
- W3199342842 hasConceptScore W3199342842C10551718 @default.
- W3199342842 hasConceptScore W3199342842C105795698 @default.
- W3199342842 hasConceptScore W3199342842C112633086 @default.