Deep learning-based neural networks for day-ahead power load probability density forecasting.

Zhou, Yanlai; Zhu, Di; Chen, Hua; Guo, Shenglian; Xu, Chong-Yu; Chang, Fi-John

Zhou, Yanlai; Zhu, Di; Chen, Hua; Guo, Shenglian; Xu, Chong-Yu; Chang, Fi-John.

Afiliação

Zhou Y; State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China. yanlai.zhou@whu.edu.cn.
Zhu D; State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China.
Chen H; State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China.
Guo S; State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China.
Xu CY; Department of Geosciences, University of Oslo, Blindern, P.O. Box 1047, N-0316, Oslo, Norway.
Chang FJ; Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan.

Environ Sci Pollut Res Int ; 30(7): 17741-17764, 2023 Feb.

Article em En | MEDLINE | ID: mdl-36201077

Assuntos

Aprendizado Profundo; Gases de Efeito Estufa; Reprodutibilidade dos Testes; Redes Neurais de Computação; Previsões; Probabilidade

Palavras-chave

Deep learning; Energy efficiency; Monotone composite quantile regression neural network (MCQRNN); Probability density forecast; Regional power load; Smart grid

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Gases de Efeito Estufa / Aprendizado Profundo Tipo de estudo: Prognostic_studies Idioma: En Revista: Environ Sci Pollut Res Int Assunto da revista: SAUDE AMBIENTAL / TOXICOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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