Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM

Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is bas...
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Autor*in:	Xuechao Liao [verfasserIn] Zhenxing Liu [verfasserIn] Wanxiong Deng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	attention mechanism LSTM (long‐short term memory) short‐term wind speed forecast VMD (variational mode decomposition) wavelet decomposition and reconstruction

Übergeordnetes Werk:	In: Wind Energy - Wiley, 2021, 24(2021), 9, Seite 991-1012
Übergeordnetes Werk:	volume:24 ; year:2021 ; number:9 ; pages:991-1012

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1002/we.2613

Katalog-ID:	DOAJ005041856

Internformat


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520			\|a Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction.
650		4	\|a attention mechanism
650		4	\|a LSTM (long‐short term memory)
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650		4	\|a VMD (variational mode decomposition)
650		4	\|a wavelet decomposition and reconstruction
653		0	\|a Renewable energy sources
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allfields	10.1002/we.2613 doi (DE-627)DOAJ005041856 (DE-599)DOAJcd38f6e54df94c3fb4edc03563799591 DE-627 ger DE-627 rakwb eng TJ807-830 Xuechao Liao verfasserin aut Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction. attention mechanism LSTM (long‐short term memory) short‐term wind speed forecast VMD (variational mode decomposition) wavelet decomposition and reconstruction Renewable energy sources Zhenxing Liu verfasserin aut Wanxiong Deng verfasserin aut In Wind Energy Wiley, 2021 24(2021), 9, Seite 991-1012 (DE-627)319418448 (DE-600)2024840-4 10991824 nnns volume:24 year:2021 number:9 pages:991-1012 https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/article/cd38f6e54df94c3fb4edc03563799591 kostenfrei https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/toc/1095-4244 Journal toc kostenfrei https://doaj.org/toc/1099-1824 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2021 9 991-1012
spelling	10.1002/we.2613 doi (DE-627)DOAJ005041856 (DE-599)DOAJcd38f6e54df94c3fb4edc03563799591 DE-627 ger DE-627 rakwb eng TJ807-830 Xuechao Liao verfasserin aut Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction. attention mechanism LSTM (long‐short term memory) short‐term wind speed forecast VMD (variational mode decomposition) wavelet decomposition and reconstruction Renewable energy sources Zhenxing Liu verfasserin aut Wanxiong Deng verfasserin aut In Wind Energy Wiley, 2021 24(2021), 9, Seite 991-1012 (DE-627)319418448 (DE-600)2024840-4 10991824 nnns volume:24 year:2021 number:9 pages:991-1012 https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/article/cd38f6e54df94c3fb4edc03563799591 kostenfrei https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/toc/1095-4244 Journal toc kostenfrei https://doaj.org/toc/1099-1824 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2021 9 991-1012
allfields_unstemmed	10.1002/we.2613 doi (DE-627)DOAJ005041856 (DE-599)DOAJcd38f6e54df94c3fb4edc03563799591 DE-627 ger DE-627 rakwb eng TJ807-830 Xuechao Liao verfasserin aut Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction. attention mechanism LSTM (long‐short term memory) short‐term wind speed forecast VMD (variational mode decomposition) wavelet decomposition and reconstruction Renewable energy sources Zhenxing Liu verfasserin aut Wanxiong Deng verfasserin aut In Wind Energy Wiley, 2021 24(2021), 9, Seite 991-1012 (DE-627)319418448 (DE-600)2024840-4 10991824 nnns volume:24 year:2021 number:9 pages:991-1012 https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/article/cd38f6e54df94c3fb4edc03563799591 kostenfrei https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/toc/1095-4244 Journal toc kostenfrei https://doaj.org/toc/1099-1824 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2021 9 991-1012
allfieldsGer	10.1002/we.2613 doi (DE-627)DOAJ005041856 (DE-599)DOAJcd38f6e54df94c3fb4edc03563799591 DE-627 ger DE-627 rakwb eng TJ807-830 Xuechao Liao verfasserin aut Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction. attention mechanism LSTM (long‐short term memory) short‐term wind speed forecast VMD (variational mode decomposition) wavelet decomposition and reconstruction Renewable energy sources Zhenxing Liu verfasserin aut Wanxiong Deng verfasserin aut In Wind Energy Wiley, 2021 24(2021), 9, Seite 991-1012 (DE-627)319418448 (DE-600)2024840-4 10991824 nnns volume:24 year:2021 number:9 pages:991-1012 https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/article/cd38f6e54df94c3fb4edc03563799591 kostenfrei https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/toc/1095-4244 Journal toc kostenfrei https://doaj.org/toc/1099-1824 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2021 9 991-1012
allfieldsSound	10.1002/we.2613 doi (DE-627)DOAJ005041856 (DE-599)DOAJcd38f6e54df94c3fb4edc03563799591 DE-627 ger DE-627 rakwb eng TJ807-830 Xuechao Liao verfasserin aut Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction. attention mechanism LSTM (long‐short term memory) short‐term wind speed forecast VMD (variational mode decomposition) wavelet decomposition and reconstruction Renewable energy sources Zhenxing Liu verfasserin aut Wanxiong Deng verfasserin aut In Wind Energy Wiley, 2021 24(2021), 9, Seite 991-1012 (DE-627)319418448 (DE-600)2024840-4 10991824 nnns volume:24 year:2021 number:9 pages:991-1012 https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/article/cd38f6e54df94c3fb4edc03563799591 kostenfrei https://doi.org/10.1002/we.2613 kostenfrei https://doaj.org/toc/1095-4244 Journal toc kostenfrei https://doaj.org/toc/1099-1824 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2021 9 991-1012
language	English
source	In Wind Energy 24(2021), 9, Seite 991-1012 volume:24 year:2021 number:9 pages:991-1012
sourceStr	In Wind Energy 24(2021), 9, Seite 991-1012 volume:24 year:2021 number:9 pages:991-1012
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	attention mechanism LSTM (long‐short term memory) short‐term wind speed forecast VMD (variational mode decomposition) wavelet decomposition and reconstruction Renewable energy sources
isfreeaccess_bool	true
container_title	Wind Energy
authorswithroles_txt_mv	Xuechao Liao @@aut@@ Zhenxing Liu @@aut@@ Wanxiong Deng @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	319418448
id	DOAJ005041856
language_de	englisch
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callnumber-first	T - Technology
author	Xuechao Liao
spellingShingle	Xuechao Liao misc TJ807-830 misc attention mechanism misc LSTM (long‐short term memory) misc short‐term wind speed forecast misc VMD (variational mode decomposition) misc wavelet decomposition and reconstruction misc Renewable energy sources Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM
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topic_title	TJ807-830 Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM attention mechanism LSTM (long‐short term memory) short‐term wind speed forecast VMD (variational mode decomposition) wavelet decomposition and reconstruction
topic	misc TJ807-830 misc attention mechanism misc LSTM (long‐short term memory) misc short‐term wind speed forecast misc VMD (variational mode decomposition) misc wavelet decomposition and reconstruction misc Renewable energy sources
topic_unstemmed	misc TJ807-830 misc attention mechanism misc LSTM (long‐short term memory) misc short‐term wind speed forecast misc VMD (variational mode decomposition) misc wavelet decomposition and reconstruction misc Renewable energy sources
topic_browse	misc TJ807-830 misc attention mechanism misc LSTM (long‐short term memory) misc short‐term wind speed forecast misc VMD (variational mode decomposition) misc wavelet decomposition and reconstruction misc Renewable energy sources
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title	Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM
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title_full	Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM
author_sort	Xuechao Liao
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author_browse	Xuechao Liao Zhenxing Liu Wanxiong Deng
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doi_str_mv	10.1002/we.2613
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title_sort	short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and lstm
callnumber	TJ807-830
title_auth	Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM
abstract	Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction.
abstractGer	Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction.
abstract_unstemmed	Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction.
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title_short	Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM
url	https://doi.org/10.1002/we.2613 https://doaj.org/article/cd38f6e54df94c3fb4edc03563799591 https://doaj.org/toc/1095-4244 https://doaj.org/toc/1099-1824
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doi_str	10.1002/we.2613
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up_date	2024-07-04T02:07:09.495Z
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Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM

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