Multi-step interval prediction of ultra-short-term wind power based on CEEMDAN-FIG and CNN-BiLSTM.

Zhao, Zheng; Nan, Honggang; Liu, Zihan; Yu, Yuebo

Zhao, Zheng; Nan, Honggang; Liu, Zihan; Yu, Yuebo.

Afiliación

Zhao Z; School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, Hebei, China. zheng_zhao@ncepu.edu.cn.
Nan H; School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, Hebei, China.
Liu Z; School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, Hebei, China.
Yu Y; School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, Hebei, China.

Environ Sci Pollut Res Int ; 29(38): 58097-58109, 2022 Aug.

Article en En | MEDLINE | ID: mdl-35362890

Asunto(s)

Ficus; Predicción; Redes Neurales de la Computación; Incertidumbre

Palabras clave

CEEMDAN; CNN-BiLSTM; Fuzzy information granulation; Interval prediction; Wind power

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ficus Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Sci Pollut Res Int Asunto de la revista: SAUDE AMBIENTAL / TOXICOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania

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