A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting
A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting
About this item
Full title
Author / Creator
Liu, Jie , Shi, Quan , Han, Ruilian and Yang, Juan
Publisher
Basel: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Basel: MDPI AG
Subjects
More information
Scope and Contents
Contents
Accurate and timely wind power forecasting is essential for achieving large-scale wind power grid integration and ensuring the safe and stable operation of the power system. For overcoming the inaccuracy of wind power forecasting caused by randomness and volatility, this study proposes a hybrid convolutional neural network (CNN) model (GA–PSO–CNN)...
Alternative Titles
Full title
A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_ba113bd4c8c64327a60d7497638ea5b3
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ba113bd4c8c64327a60d7497638ea5b3
Other Identifiers
ISSN
1996-1073
E-ISSN
1996-1073
DOI
10.3390/en14206500
