Matches in SemOpenAlex for { <https://semopenalex.org/work/W3089243611> ?p ?o ?g. }
- W3089243611 endingPage "4964" @default.
- W3089243611 startingPage "4964" @default.
- W3089243611 abstract "Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years." @default.
- W3089243611 created "2020-10-01" @default.
- W3089243611 creator A5021155429 @default.
- W3089243611 creator A5061377547 @default.
- W3089243611 creator A5080583315 @default.
- W3089243611 creator A5082419562 @default.
- W3089243611 creator A5091244614 @default.
- W3089243611 date "2020-09-22" @default.
- W3089243611 modified "2023-10-18" @default.
- W3089243611 title "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model" @default.
- W3089243611 cites W2522643454 @default.
- W3089243611 cites W2737765213 @default.
- W3089243611 cites W2762869206 @default.
- W3089243611 cites W2766769613 @default.
- W3089243611 cites W2775425027 @default.
- W3089243611 cites W2791937529 @default.
- W3089243611 cites W2794103404 @default.
- W3089243611 cites W2794393856 @default.
- W3089243611 cites W2795386923 @default.
- W3089243611 cites W2801465633 @default.
- W3089243611 cites W2802924253 @default.
- W3089243611 cites W2884538367 @default.
- W3089243611 cites W2886292843 @default.
- W3089243611 cites W2893815961 @default.
- W3089243611 cites W2895581311 @default.
- W3089243611 cites W2896742282 @default.
- W3089243611 cites W2896920734 @default.
- W3089243611 cites W2902252637 @default.
- W3089243611 cites W2903162817 @default.
- W3089243611 cites W2908011737 @default.
- W3089243611 cites W2912157889 @default.
- W3089243611 cites W2922403949 @default.
- W3089243611 cites W2925057617 @default.
- W3089243611 cites W2936236242 @default.
- W3089243611 cites W2946155684 @default.
- W3089243611 cites W2950895399 @default.
- W3089243611 cites W2955529518 @default.
- W3089243611 cites W2966392392 @default.
- W3089243611 cites W2973960501 @default.
- W3089243611 cites W2977676336 @default.
- W3089243611 cites W2980223420 @default.
- W3089243611 cites W2981840557 @default.
- W3089243611 cites W2983772465 @default.
- W3089243611 cites W2987655629 @default.
- W3089243611 cites W2990255447 @default.
- W3089243611 cites W2997373520 @default.
- W3089243611 cites W2999868636 @default.
- W3089243611 cites W3001566518 @default.
- W3089243611 cites W3015799856 @default.
- W3089243611 cites W3021291274 @default.
- W3089243611 cites W3046160224 @default.
- W3089243611 doi "https://doi.org/10.3390/en13184964" @default.
- W3089243611 hasPublicationYear "2020" @default.
- W3089243611 type Work @default.
- W3089243611 sameAs 3089243611 @default.
- W3089243611 citedByCount "33" @default.
- W3089243611 countsByYear W30892436112021 @default.
- W3089243611 countsByYear W30892436112022 @default.
- W3089243611 countsByYear W30892436112023 @default.
- W3089243611 crossrefType "journal-article" @default.
- W3089243611 hasAuthorship W3089243611A5021155429 @default.
- W3089243611 hasAuthorship W3089243611A5061377547 @default.
- W3089243611 hasAuthorship W3089243611A5080583315 @default.
- W3089243611 hasAuthorship W3089243611A5082419562 @default.
- W3089243611 hasAuthorship W3089243611A5091244614 @default.
- W3089243611 hasBestOaLocation W30892436111 @default.
- W3089243611 hasConcept C108583219 @default.
- W3089243611 hasConcept C119599485 @default.
- W3089243611 hasConcept C119857082 @default.
- W3089243611 hasConcept C121332964 @default.
- W3089243611 hasConcept C127313418 @default.
- W3089243611 hasConcept C127413603 @default.
- W3089243611 hasConcept C143724316 @default.
- W3089243611 hasConcept C149782125 @default.
- W3089243611 hasConcept C151406439 @default.
- W3089243611 hasConcept C151730666 @default.
- W3089243611 hasConcept C154945302 @default.
- W3089243611 hasConcept C163258240 @default.
- W3089243611 hasConcept C188573790 @default.
- W3089243611 hasConcept C2781084341 @default.
- W3089243611 hasConcept C33923547 @default.
- W3089243611 hasConcept C41008148 @default.
- W3089243611 hasConcept C423512 @default.
- W3089243611 hasConcept C50644808 @default.
- W3089243611 hasConcept C62520636 @default.
- W3089243611 hasConcept C78600449 @default.
- W3089243611 hasConcept C89227174 @default.
- W3089243611 hasConcept C91602232 @default.
- W3089243611 hasConceptScore W3089243611C108583219 @default.
- W3089243611 hasConceptScore W3089243611C119599485 @default.
- W3089243611 hasConceptScore W3089243611C119857082 @default.
- W3089243611 hasConceptScore W3089243611C121332964 @default.
- W3089243611 hasConceptScore W3089243611C127313418 @default.
- W3089243611 hasConceptScore W3089243611C127413603 @default.
- W3089243611 hasConceptScore W3089243611C143724316 @default.
- W3089243611 hasConceptScore W3089243611C149782125 @default.
- W3089243611 hasConceptScore W3089243611C151406439 @default.
- W3089243611 hasConceptScore W3089243611C151730666 @default.