Wind power prediction method based on CNN-LSTM-Attention and Bayesian optimization
Author:
Affiliation:
1. Jianghan University,School of Artificial Intelligence,Wuhan,China
2. Jianghan University,School of Optoelectronic Materials and Technology,Wuhan,China
Funder
Jianghan University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10574856/10574899/10575340.pdf?arnumber=10575340
Reference12 articles.
1. The temporal variability of global wind energy – Long-term trends and inter-annual variability
2. A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing
3. Application of distributed predictive control in coordinated control of microgrid [J];MA;Journal of Jilin University (Engineering and Technology Edition),2020
4. Wind power forecast based on variational mode decomposition and long short term memory attention network
5. Research on Short-Term Load Prediction Based on Seq2seq Model
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