Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization

•A novel nonlinear-learning ensemble of deep learning time series prediction is proposed for wind speed forecasting.•A cluster of LSTMs with diverse hidden layers and neurons are introduced to explore and exploit the wind speed time series.•One nonlinear-learning regression top-layer composed of SVR...

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Veröffentlicht in:	Energy conversion and management Jg. 165; S. 681 - 695
Hauptverfasser:	Chen, Jie, Zeng, Guo-Qiang, Zhou, Wuneng, Du, Wei, Lu, Kang-Di
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 01.06.2018 Elsevier Science Ltd
Schlagworte:	algorithms artificial intelligence case studies China data collection Deep learning Electricity generation Ensemble learning Extremal optimization Forecasting Long short-term memory LSTMs (Long Short Term Memory neural networks) Neural networks Optimization prediction Prediction models Statistical analysis Statistical tests Support vector machines Time series time series analysis Time series prediction Velocity Wind farms Wind power Wind speed Wind speed forecasting China Deep learning Time series prediction Ensemble learning LSTMs (Long Short Term Memory neural networks) Extremal optimization Wind speed forecasting
ISSN:	0196-8904, 1879-2227
Online-Zugang:	Volltext
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