Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition
Abstract
:1. Introduction
2. Mathematical Background
2.1. VMD
2.2. TCN
2.2.1. Dilated Causal Convolution (DCC)
2.2.2. Residual Connections
2.3. Short-Term Wind Power Forecasting Model Based on VMD-TCN
3. Example Simulation Design
3.1. Data Pre-Processing
3.1.1. Data Cleaning
3.1.2. Data Normalization
3.2. Model Performance Evaluation Indexes
4. Simulation and Result Analysis
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Weather | Clear | Clouds | Drizzle | Fog | Haze | Mist | Rain | Smoke | Thunderstorm |
---|---|---|---|---|---|---|---|---|---|
Code | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
Evaluation Indicators | VMD-TCN | VMD-LSTM | TCN | LSTM |
---|---|---|---|---|
MAE (W) | 64.91 | 68.90 | 81.36 | 128.17 |
MAPE | 2.79% | 3.13% | 3.67% | 5.47% |
RMSE (W) | 74.13 | 80.55 | 93.87 | 144.95 |
R2 | 0.9985 | 0.9928 | 0.9950 | 0.9938 |
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Tang, J.; Chien, Y.-R. Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition. Sensors 2022, 22, 7414. https://doi.org/10.3390/s22197414
Tang J, Chien Y-R. Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition. Sensors. 2022; 22(19):7414. https://doi.org/10.3390/s22197414
Chicago/Turabian StyleTang, Jingwei, and Ying-Ren Chien. 2022. "Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition" Sensors 22, no. 19: 7414. https://doi.org/10.3390/s22197414
APA StyleTang, J., & Chien, Y.-R. (2022). Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition. Sensors, 22(19), 7414. https://doi.org/10.3390/s22197414