Wind Power Forecasting using Hybrid Recurrent Neural Networks with Empirical Mode Decomposition
Author:
Affiliation:
1. Stellenbosch University,Electrical and Electronic Engineering,Stellenbosch,South Africa
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9854509/9854408/09854798.pdf?arnumber=9854798
Reference16 articles.
1. Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics
2. Wind power forecasting – A data-driven method along with gated recurrent neural network
3. A Review of Modern Wind Turbine Technology
4. On empirical mode decomposition and its algorithms;rilling;Proceedings of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03,0
5. Global Adaptive Input Normalization for Short-Term Electric Load Forecasting
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1. Short-term wind power forecasting based on dual attention mechanism and gated recurrent unit neural network;Frontiers in Energy Research;2024-01-18
2. Renewable Energy System Optimization Decision Method Considering Wind Power Prediction;Lecture Notes in Electrical Engineering;2024
3. Temporal Fusion Transformer for Day-Ahead Wind Power Forecasting in the South African Context;2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe);2023-06-06
4. Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network;Sustainability;2023-06-05
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