Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation
Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and chall...
Ausführliche Beschreibung
Autor*in: |
Li, Yang [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Anmerkung: |
© The Author(s) 2023 |
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Übergeordnetes Werk: |
Enthalten in: Electrical engineering - Berlin : Springer, 1912, 106(2023), 1 vom: 11. Sept., Seite 655-671 |
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Übergeordnetes Werk: |
volume:106 ; year:2023 ; number:1 ; day:11 ; month:09 ; pages:655-671 |
Links: |
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DOI / URN: |
10.1007/s00202-023-02005-z |
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Katalog-ID: |
SPR054762529 |
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520 | |a Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. | ||
650 | 4 | |a Artificial intelligence |7 (dpeaa)DE-He213 | |
650 | 4 | |a Aggregated wind power characteristics |7 (dpeaa)DE-He213 | |
650 | 4 | |a Regional wind power |7 (dpeaa)DE-He213 | |
700 | 1 | |a Janik, Przemysław |4 aut | |
700 | 1 | |a Schwarz, Harald |4 aut | |
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10.1007/s00202-023-02005-z doi (DE-627)SPR054762529 (SPR)s00202-023-02005-z-e DE-627 ger DE-627 rakwb eng Li, Yang verfasserin (orcid)0000-0001-8070-2704 aut Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. Artificial intelligence (dpeaa)DE-He213 Aggregated wind power characteristics (dpeaa)DE-He213 Regional wind power (dpeaa)DE-He213 Janik, Przemysław aut Schwarz, Harald aut Enthalten in Electrical engineering Berlin : Springer, 1912 106(2023), 1 vom: 11. Sept., Seite 655-671 (DE-627)27159926X (DE-600)1480921-7 1432-0487 nnns volume:106 year:2023 number:1 day:11 month:09 pages:655-671 https://dx.doi.org/10.1007/s00202-023-02005-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 106 2023 1 11 09 655-671 |
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10.1007/s00202-023-02005-z doi (DE-627)SPR054762529 (SPR)s00202-023-02005-z-e DE-627 ger DE-627 rakwb eng Li, Yang verfasserin (orcid)0000-0001-8070-2704 aut Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. Artificial intelligence (dpeaa)DE-He213 Aggregated wind power characteristics (dpeaa)DE-He213 Regional wind power (dpeaa)DE-He213 Janik, Przemysław aut Schwarz, Harald aut Enthalten in Electrical engineering Berlin : Springer, 1912 106(2023), 1 vom: 11. Sept., Seite 655-671 (DE-627)27159926X (DE-600)1480921-7 1432-0487 nnns volume:106 year:2023 number:1 day:11 month:09 pages:655-671 https://dx.doi.org/10.1007/s00202-023-02005-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 106 2023 1 11 09 655-671 |
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10.1007/s00202-023-02005-z doi (DE-627)SPR054762529 (SPR)s00202-023-02005-z-e DE-627 ger DE-627 rakwb eng Li, Yang verfasserin (orcid)0000-0001-8070-2704 aut Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. Artificial intelligence (dpeaa)DE-He213 Aggregated wind power characteristics (dpeaa)DE-He213 Regional wind power (dpeaa)DE-He213 Janik, Przemysław aut Schwarz, Harald aut Enthalten in Electrical engineering Berlin : Springer, 1912 106(2023), 1 vom: 11. Sept., Seite 655-671 (DE-627)27159926X (DE-600)1480921-7 1432-0487 nnns volume:106 year:2023 number:1 day:11 month:09 pages:655-671 https://dx.doi.org/10.1007/s00202-023-02005-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 106 2023 1 11 09 655-671 |
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10.1007/s00202-023-02005-z doi (DE-627)SPR054762529 (SPR)s00202-023-02005-z-e DE-627 ger DE-627 rakwb eng Li, Yang verfasserin (orcid)0000-0001-8070-2704 aut Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. Artificial intelligence (dpeaa)DE-He213 Aggregated wind power characteristics (dpeaa)DE-He213 Regional wind power (dpeaa)DE-He213 Janik, Przemysław aut Schwarz, Harald aut Enthalten in Electrical engineering Berlin : Springer, 1912 106(2023), 1 vom: 11. Sept., Seite 655-671 (DE-627)27159926X (DE-600)1480921-7 1432-0487 nnns volume:106 year:2023 number:1 day:11 month:09 pages:655-671 https://dx.doi.org/10.1007/s00202-023-02005-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 106 2023 1 11 09 655-671 |
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10.1007/s00202-023-02005-z doi (DE-627)SPR054762529 (SPR)s00202-023-02005-z-e DE-627 ger DE-627 rakwb eng Li, Yang verfasserin (orcid)0000-0001-8070-2704 aut Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. Artificial intelligence (dpeaa)DE-He213 Aggregated wind power characteristics (dpeaa)DE-He213 Regional wind power (dpeaa)DE-He213 Janik, Przemysław aut Schwarz, Harald aut Enthalten in Electrical engineering Berlin : Springer, 1912 106(2023), 1 vom: 11. Sept., Seite 655-671 (DE-627)27159926X (DE-600)1480921-7 1432-0487 nnns volume:106 year:2023 number:1 day:11 month:09 pages:655-671 https://dx.doi.org/10.1007/s00202-023-02005-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 106 2023 1 11 09 655-671 |
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Enthalten in Electrical engineering 106(2023), 1 vom: 11. Sept., Seite 655-671 volume:106 year:2023 number:1 day:11 month:09 pages:655-671 |
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Enthalten in Electrical engineering 106(2023), 1 vom: 11. Sept., Seite 655-671 volume:106 year:2023 number:1 day:11 month:09 pages:655-671 |
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Li, Yang @@aut@@ Janik, Przemysław @@aut@@ Schwarz, Harald @@aut@@ |
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Li, Yang |
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Li, Yang misc Artificial intelligence misc Aggregated wind power characteristics misc Regional wind power Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation |
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Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation Artificial intelligence (dpeaa)DE-He213 Aggregated wind power characteristics (dpeaa)DE-He213 Regional wind power (dpeaa)DE-He213 |
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Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation |
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aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation |
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Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation |
abstract |
Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. © The Author(s) 2023 |
abstractGer |
Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. © The Author(s) 2023 |
abstract_unstemmed |
Abstract The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction. © The Author(s) 2023 |
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Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation |
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https://dx.doi.org/10.1007/s00202-023-02005-z |
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In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. 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