Improved ultra short term wind speed prediction based on BILSTM and meta feature driven Ensemble learning

Name: Improved ultra short term wind speed prediction based on BILSTM and meta feature driven Ensemble learning
Creator: Pengxiang Sun
Published: 2023-10-25T15:10:47.342Z
Keywords: Wind Energy

Sun, Pengxiang

doi:10.17632/k37dtysknm.1

Improved ultra short term wind speed prediction based on BILSTM and meta feature driven Ensemble learning

Published: 25 October 2023| Version 1 | DOI: 10.17632/k37dtysknm.1

Contributor:

Pengxiang Sun

Description

We collected wind speed data from four different wind farms at different altitudes in the United States. These data contain changes in wind speed at different altitudes, helping us understand the impact of different altitudes on wind speed.

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Steps to reproduce

The dataset used in this study is available for download at [https://mesonet.agron.iastate.edu/request/asos/1min.phtml]. No specific data preprocessing steps were applied to the raw data.

Institutions

Xi'an University of Architecture and Technology

Improved ultra short term wind speed prediction based on BILSTM and meta feature driven Ensemble learning

Description

Files

Steps to reproduce

Institutions

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