Short-Term Wind Power Prediction Based on CEEMDAN and Parallel CNN-LSTM
Author:
Affiliation:
1. Huazhong University of Science and Technology,School of Electrical and Electronic Engineering,Wuhan,China
Funder
State Grid Corporation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9948701/9949620/09949917.pdf?arnumber=9949917
Reference18 articles.
1. Learning Heterogeneous Features Jointly: A Deep End-to-End Framework for Multi-Step Short-Term Wind Power Prediction
2. Improved EMD-Based Complex Prediction Model for Wind Power Forecasting
3. Short-Term Wind Power Forecasting Based on VMD Decomposition, ConvLSTM Networks and Error Analysis
4. Wind Speed Forecasting Using the Stationary Wavelet Transform and Quaternion Adaptive-Gradient Methods
5. Dispatch Planning of a Wide-Area Wind Power-Energy Storage Scheme Based on Ensemble Empirical Mode Decomposition Technique
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1. Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN;Energy;2024-07
2. Economic optimal control of source-storage collaboration based on wind power forecasting for transient frequency regulation;Journal of Energy Storage;2024-04
3. A Hybrid Model Based on Complete Ensemble Empirical Mode Decomposition With Adaptive Noise, GRU Network and Whale Optimization Algorithm for Wind Power Prediction;IEEE Access;2023
4. Short-Term Voltage Stability Online Assessment Based on Energy Function;2023
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