LS-LSTM-AE: Power load forecasting via Long-Short series features and LSTM-Autoencoder

LS-LSTM-AE: Power load forecasting via Long-Short series features and LSTM-Autoencoder

Aiming at weak representation ability and severe loss of time series features in the traditional methods when facing large-scale and complex power load forecasting tasks, an LSTM-Autoencoder model that integrates long-term and short-term features of the samples is proposed for load forecasting. The...

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Detalhes bibliográficos
Main Authors: Xin Tong, Jingya Wang, Changlin Zhang, Teng Wu, Haitao Wang, Yu Wang
Formato: Artigo
Idioma:English
Publicado em: Elsevier 2022-04-01
Colecção:Energy Reports
Assuntos:
Load forecasting
Deep learning
LSTM
Autoencoder
Unsupervised learning
Supervised learning
Acesso em linha:http://www.sciencedirect.com/science/article/pii/S2352484721013196

Internet

http://www.sciencedirect.com/science/article/pii/S2352484721013196

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