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Ultra-short-term power forecast of photovoltaic power station based on VMD–LSTM model optimised by SSA

Jing Yizhou

Jing Yizhou

College of Electrical Engineering & New Energy, China Three Gorges UniversityYichang, China
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Yizhou, Jing
, 
Yang Siqi

Yang Siqi

College of Electrical Engineering & New Energy, China Three Gorges UniversityYichang, China
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Siqi, Yang
 e 
Zhang Kegeng

Zhang Kegeng

College of Electrical Engineering & New Energy, China Three Gorges UniversityYichang, China
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Kegeng, Zhang
  
05 set 2022
Ultra-short-term power forecast of photovoltaic power station based on VMD–LSTM model optimised by SSA's Cover Image
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Pubblicato online: 05 set 2022
Pagine: 823 - 834
Ricevuto: 25 apr 2022
Accettato: 15 giu 2022
DOI: https://doi.org/10.2478/amns.2021.2.00246
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© 2023 Jing Yizhou et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Influence of weather types on photovoltaic power
Influence of weather types on photovoltaic power

Fig. 2

VMD decomposition results
VMD decomposition results

Fig. 3

LSTM network structure.LSTM, long short-term memory
LSTM network structure.LSTM, long short-term memory

Fig. 4

Forecast process
Forecast process

Fig. 5

Forecast results of photovoltaic power on a sunny day on November 7
Forecast results of photovoltaic power on a sunny day on November 7

Fig. 6

Forecast results of photovoltaic power on a cloudy day of June 5
Forecast results of photovoltaic power on a cloudy day of June 5

Fig. 7

Forecast results of sudden change weather on January 8
Forecast results of sudden change weather on January 8
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
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