A hybrid approach to ultra short-term wind speed prediction using CEEMDAN and Informer
Author:
Affiliation:
1. NIT Andhra Pradesh,Dept. of Electrical Engineering,Tadepalligudem,India
2. EPIC, North Carolina,Charlotte,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10068857/10068873/10069064.pdf?arnumber=10069064
Reference21 articles.
1. Ultra-Short-Term Wind Speed Forecasting Based on Meta Learning with Signal Trend and Fluctuation Decomposition
2. Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction
3. Short-term wind speed predicting framework based on EEMD-GA-LSTM method under large scaled wind history
4. A novel ensemble model based on artificial intelligence and mixed-frequency techniques for wind speed forecasting
5. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
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