A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power

Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The or...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hai-Kun Wang [verfasserIn] Ke Song [verfasserIn] Yi Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	average wind power prediction long sequence input prediction convolution informer A hybrid method

Übergeordnetes Werk:	In: Frontiers in Energy Research - Frontiers Media S.A., 2014, 9(2022)
Übergeordnetes Werk:	volume:9 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fenrg.2021.788320

Katalog-ID:	DOAJ061417637

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ061417637
003	DE-627
005	20230309011730.0
007	cr uuu---uuuuu
008	230228s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3389/fenrg.2021.788320 \|2 doi
035			\|a (DE-627)DOAJ061417637
035			\|a (DE-599)DOAJ6cd3b68c4fb34f9e9d91c7beb344314a
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Hai-Kun Wang \|e verfasserin \|4 aut
245	1	2	\|a A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy.
650		4	\|a average wind power prediction
650		4	\|a long sequence input prediction
650		4	\|a convolution
650		4	\|a informer
650		4	\|a A hybrid method
653		0	\|a General Works
653		0	\|a A
700	0		\|a Hai-Kun Wang \|e verfasserin \|4 aut
700	0		\|a Ke Song \|e verfasserin \|4 aut
700	0		\|a Yi Cheng \|e verfasserin \|4 aut
773	0	8	\|i In \|t Frontiers in Energy Research \|d Frontiers Media S.A., 2014 \|g 9(2022) \|w (DE-627)768576768 \|w (DE-600)2733788-1 \|x 2296598X \|7 nnns
773	1	8	\|g volume:9 \|g year:2022
856	4	0	\|u https://doi.org/10.3389/fenrg.2021.788320 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/6cd3b68c4fb34f9e9d91c7beb344314a \|z kostenfrei
856	4	0	\|u https://www.frontiersin.org/articles/10.3389/fenrg.2021.788320/full \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2296-598X \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 9 \|j 2022

Indexfelder

author_variant	h k w hkw h k w hkw k s ks y c yc
matchkey_str	article:2296598X:2022----::hbifrcsigoebsdnnadnomros
hierarchy_sort_str	2022
publishDate	2022
allfields	10.3389/fenrg.2021.788320 doi (DE-627)DOAJ061417637 (DE-599)DOAJ6cd3b68c4fb34f9e9d91c7beb344314a DE-627 ger DE-627 rakwb eng Hai-Kun Wang verfasserin aut A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy. average wind power prediction long sequence input prediction convolution informer A hybrid method General Works A Hai-Kun Wang verfasserin aut Ke Song verfasserin aut Yi Cheng verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2022) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2022 https://doi.org/10.3389/fenrg.2021.788320 kostenfrei https://doaj.org/article/6cd3b68c4fb34f9e9d91c7beb344314a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.788320/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
spelling	10.3389/fenrg.2021.788320 doi (DE-627)DOAJ061417637 (DE-599)DOAJ6cd3b68c4fb34f9e9d91c7beb344314a DE-627 ger DE-627 rakwb eng Hai-Kun Wang verfasserin aut A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy. average wind power prediction long sequence input prediction convolution informer A hybrid method General Works A Hai-Kun Wang verfasserin aut Ke Song verfasserin aut Yi Cheng verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2022) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2022 https://doi.org/10.3389/fenrg.2021.788320 kostenfrei https://doaj.org/article/6cd3b68c4fb34f9e9d91c7beb344314a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.788320/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
allfields_unstemmed	10.3389/fenrg.2021.788320 doi (DE-627)DOAJ061417637 (DE-599)DOAJ6cd3b68c4fb34f9e9d91c7beb344314a DE-627 ger DE-627 rakwb eng Hai-Kun Wang verfasserin aut A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy. average wind power prediction long sequence input prediction convolution informer A hybrid method General Works A Hai-Kun Wang verfasserin aut Ke Song verfasserin aut Yi Cheng verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2022) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2022 https://doi.org/10.3389/fenrg.2021.788320 kostenfrei https://doaj.org/article/6cd3b68c4fb34f9e9d91c7beb344314a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.788320/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
allfieldsGer	10.3389/fenrg.2021.788320 doi (DE-627)DOAJ061417637 (DE-599)DOAJ6cd3b68c4fb34f9e9d91c7beb344314a DE-627 ger DE-627 rakwb eng Hai-Kun Wang verfasserin aut A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy. average wind power prediction long sequence input prediction convolution informer A hybrid method General Works A Hai-Kun Wang verfasserin aut Ke Song verfasserin aut Yi Cheng verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2022) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2022 https://doi.org/10.3389/fenrg.2021.788320 kostenfrei https://doaj.org/article/6cd3b68c4fb34f9e9d91c7beb344314a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.788320/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
allfieldsSound	10.3389/fenrg.2021.788320 doi (DE-627)DOAJ061417637 (DE-599)DOAJ6cd3b68c4fb34f9e9d91c7beb344314a DE-627 ger DE-627 rakwb eng Hai-Kun Wang verfasserin aut A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy. average wind power prediction long sequence input prediction convolution informer A hybrid method General Works A Hai-Kun Wang verfasserin aut Ke Song verfasserin aut Yi Cheng verfasserin aut In Frontiers in Energy Research Frontiers Media S.A., 2014 9(2022) (DE-627)768576768 (DE-600)2733788-1 2296598X nnns volume:9 year:2022 https://doi.org/10.3389/fenrg.2021.788320 kostenfrei https://doaj.org/article/6cd3b68c4fb34f9e9d91c7beb344314a kostenfrei https://www.frontiersin.org/articles/10.3389/fenrg.2021.788320/full kostenfrei https://doaj.org/toc/2296-598X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
language	English
source	In Frontiers in Energy Research 9(2022) volume:9 year:2022
sourceStr	In Frontiers in Energy Research 9(2022) volume:9 year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	average wind power prediction long sequence input prediction convolution informer A hybrid method General Works A
isfreeaccess_bool	true
container_title	Frontiers in Energy Research
authorswithroles_txt_mv	Hai-Kun Wang @@aut@@ Ke Song @@aut@@ Yi Cheng @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	768576768
id	DOAJ061417637
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ061417637</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309011730.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fenrg.2021.788320</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ061417637</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6cd3b68c4fb34f9e9d91c7beb344314a</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hai-Kun Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">average wind power prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">long sequence input prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">convolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">informer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">A hybrid method</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">General Works</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">A</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hai-Kun Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ke Song</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yi Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Energy Research</subfield><subfield code="d">Frontiers Media S.A., 2014</subfield><subfield code="g">9(2022)</subfield><subfield code="w">(DE-627)768576768</subfield><subfield code="w">(DE-600)2733788-1</subfield><subfield code="x">2296598X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2022</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fenrg.2021.788320</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/6cd3b68c4fb34f9e9d91c7beb344314a</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fenrg.2021.788320/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2296-598X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2022</subfield></datafield></record></collection>
author	Hai-Kun Wang
spellingShingle	Hai-Kun Wang misc average wind power prediction misc long sequence input prediction misc convolution misc informer misc A hybrid method misc General Works misc A A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power
authorStr	Hai-Kun Wang
ppnlink_with_tag_str_mv	@@773@@(DE-627)768576768
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	2296598X
topic_title	A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power average wind power prediction long sequence input prediction convolution informer A hybrid method
topic	misc average wind power prediction misc long sequence input prediction misc convolution misc informer misc A hybrid method misc General Works misc A
topic_unstemmed	misc average wind power prediction misc long sequence input prediction misc convolution misc informer misc A hybrid method misc General Works misc A
topic_browse	misc average wind power prediction misc long sequence input prediction misc convolution misc informer misc A hybrid method misc General Works misc A
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Frontiers in Energy Research
hierarchy_parent_id	768576768
hierarchy_top_title	Frontiers in Energy Research
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)768576768 (DE-600)2733788-1
title	A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power
ctrlnum	(DE-627)DOAJ061417637 (DE-599)DOAJ6cd3b68c4fb34f9e9d91c7beb344314a
title_full	A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power
author_sort	Hai-Kun Wang
journal	Frontiers in Energy Research
journalStr	Frontiers in Energy Research
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2022
contenttype_str_mv	txt
author_browse	Hai-Kun Wang Ke Song Yi Cheng
container_volume	9
format_se	Elektronische Aufsätze
author-letter	Hai-Kun Wang
doi_str_mv	10.3389/fenrg.2021.788320
author2-role	verfasserin
title_sort	hybrid forecasting model based on cnn and informer for short-term wind power
title_auth	A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power
abstract	Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy.
abstractGer	Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy.
abstract_unstemmed	Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power
url	https://doi.org/10.3389/fenrg.2021.788320 https://doaj.org/article/6cd3b68c4fb34f9e9d91c7beb344314a https://www.frontiersin.org/articles/10.3389/fenrg.2021.788320/full https://doaj.org/toc/2296-598X
remote_bool	true
author2	Hai-Kun Wang Ke Song Yi Cheng
author2Str	Hai-Kun Wang Ke Song Yi Cheng
ppnlink	768576768
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3389/fenrg.2021.788320
up_date	2024-07-03T20:36:24.113Z
_version_	1803591597807370240
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ061417637</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309011730.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fenrg.2021.788320</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ061417637</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6cd3b68c4fb34f9e9d91c7beb344314a</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Hai-Kun Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">average wind power prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">long sequence input prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">convolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">informer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">A hybrid method</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">General Works</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">A</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hai-Kun Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ke Song</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yi Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Energy Research</subfield><subfield code="d">Frontiers Media S.A., 2014</subfield><subfield code="g">9(2022)</subfield><subfield code="w">(DE-627)768576768</subfield><subfield code="w">(DE-600)2733788-1</subfield><subfield code="x">2296598X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2022</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fenrg.2021.788320</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/6cd3b68c4fb34f9e9d91c7beb344314a</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fenrg.2021.788320/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2296-598X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2022</subfield></datafield></record></collection>
score	7.4022894

Nicht das Richtige dabei?

Schreiben Sie uns!

A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?