Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction

•Extract the hidden representation of high-resolution wind speed data.•Ensemble base predictors trained by data with different resolutions.•Obtain combination weights of base predictors by multi-objective optimization.•Reduce residual error by an adaptive decomposition-based error correction model....

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Veröffentlicht in:	Applied energy Jg. 254; S. 113686
Hauptverfasser:	Liu, Hui, Chen, Chao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.11.2019
Schlagworte:	Bidirectional long short-term memory Multi-objective multi-universe optimization Residual error correction Stacked sparse autoencoder Wind speed forecasting Stacked sparse autoencoder Bidirectional long short-term memory Multi-objective multi-universe optimization Wind speed forecasting Residual error correction
ISSN:	0306-2619, 1872-9118
Online-Zugang:	Volltext
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