Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Met...
Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods
About this item
Full title
Author / Creator
Guo, Yue , Song, Yu , Lai, Zilong , Wang, Xuyang , Wang, Licheng and Qin, Hui
Publisher
Basel: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Basel: MDPI AG
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Scope and Contents
Contents
In response to the challenges posed by renewable energy integration, this study introduces a hybrid Attention-TCN-LSTM model for short-term photovoltaic (PV) power forecasting. The LSTM captures the sequence characteristics of PV output, which are then combined with the meteorological sequence features extracted by the Attention-TCN module. The mod...
Alternative Titles
Full title
Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods
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Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_047cbcd1232d486cb1cbe43b7e54310a
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_047cbcd1232d486cb1cbe43b7e54310a
Other Identifiers
ISSN
1996-1073
E-ISSN
1996-1073
DOI
10.3390/en18020308
