An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer
Abstract
:1. Introduction
2. Methodologies
2.1. Variational Mode Decomposition
2.2. Fuzzy Entropy
2.3. Informer
2.4. Adaptive Loss Function
3. Proposed Model
3.1. Improved VMD
3.2. IVMD-FE-Ad-Informer Model Framework
3.3. Evaluation Indexes
4. Experiment and Analysis
4.1. Data Description
4.2. Experiment 1: The Specific Details of Data Processing
4.2.1. Data Decomposition
4.2.2. New Elements Reconstruction
4.2.3. Feature Selection
4.3. Experiment 2: Ablation Experiment
4.4. Experiment 3: Comparative Experiment
4.5. Experiment 4: The Stability of IVMD-FE-Ad-Informer Forecasting
5. Conclusions
- The IVMD-FE-Ad-Informer is a hybrid model that demonstrates high accuracy and better robustness by integrating the advantages of multiple technologies, outperforming the basic EMD- FE-Ad-Informer, Ad-Informer, LSTM, and ANN. The results of the proposed model obtained from the Spanish and Chinese datasets demonstrate a significant improvement compared to benchmark models, with a maximum reduction of 57.89% in MAE, 57.03% in RMSE, and a maximum increase of 30.78% in R2;
- Compared with traditional data decomposition methods, VMD improved by MIC can better mine the nonlinear features of the original data, which effectively improves the data quality and reduces the difficulty of prediction;
- Based on a comprehensive analysis of experimental results, the adaptive loss function has a rapid response to non-Gaussian distributed wind power data, which can react quickly to outliers and predict variation trends;
- By prediction experiments on wind farm datasets with different sampling intervals, capacities, and regions, the proposed model shows the best prediction results and closest proximity to the true value. It can be demonstrated that IVMD-FE-Ad-Informer has remarkable generalization ability and broad prospects in wind power prediction.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Algorithms | Parameters | Values |
---|---|---|
IVMD | K | 17 |
FE | m r | 2 0.25std |
IVMD-FE | New mode | 4(mode1 = IMF1, IMF2, IMF17; mode2 = IMF3, IMF4, IMF15, IMF16; mode3 = IMF5, IMF12~IMF14; mode4 = IMF6~IMF11) |
EMD | K | 11 |
EMD-FE | New mode | 3(mode1 = IMF1, IMF6, IMF7; mode2 = IMF2~IMF5; mode3 = IMF8~IMF11) |
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Dataset | Number | Max (MW) | Min (MW) | Mean (MW) | Std (MW) | COV |
---|---|---|---|---|---|---|
Dataset A | 8832 | 120.43 | 0 | 23.51 | 24.74 | 1.0523 |
Dataset B | 7920 | 2.803 | 0 | 0.8976 | 0.7885 | 0.8784 |
Reconstruction Elements | IMFs |
---|---|
Element 1 | IMF1, IMF2 |
Element 2 | IMF3, IMF4, IMF16 |
Element 3 | IMF5, IMF6, IMF7, IMF8, IMF15 |
Element 4 | IMF9, IMF10, IMF13, IMF14 |
Element 5 | IMF11, IMF12 |
Element | Input Variables |
---|---|
Element 1 | Element 1, 10 m, 30 m, 50 m, wheel height wind speed |
Element 2 | Element 2, 10 m, 50 m, wheel height wind speed |
Element 3 | Element 3, 30 m, 50 m, wheel height wind speed |
Element 4 | Element 4 |
Element 5 | Element 5 |
Parameters | Values |
---|---|
Input sequence length | 96 |
Start token length | 24–96 |
Prediction sequence length | 24–96 |
Num of encoder layers | 3 |
Num of decoder layers | 2 |
Input size of encoder | 5-1 |
Input size of decoder | 5-1 |
Decoder output | 1 |
Num of heads | 8 |
Dimension of model | 512 |
Probsparse attention factor | 5 |
Early stopping patience | 5 |
Learning rate | 0.0001 |
Dropout | 0.05 |
Epochs | 100 |
Scale factor | 1.2 |
Optimizer | Adam |
Gpu | Cuda0 |
Model | MAE (MW) | RMSE (MW) | R2 | Time (s) |
---|---|---|---|---|
IVMD-FE-Ad-Informer | 3.19 | 4.67 | 0.956 | 1633.21 |
Ad-Informer | 5.81 | 8.40 | 0.858 | 177.13 |
Informer | 7.91 | 10.48 | 0.779 | 165.559 |
Model | MAE (MW) | RMSE (MW) | R2 | Time (s) |
---|---|---|---|---|
IVMD-FE-Ad-Informer | 3.19 | 4.67 | 0.956 | 1633.21 |
EMD-FE-Ad-Informer | 4.96 | 7.31 | 0.905 | 1362.47 |
IVMD-FE-Informer | 5.81 | 8.40 | 0.889 | 1878.63 |
IVMD-FE-LSTM | 6.63 | 9.79 | 0.808 | 1732.57 |
LSTM | 7.86 | 10.95 | 0.759 | 305.11 |
ANN | 8.04 | 11.58 | 0.731 | 81.02 |
Model | MAE (kW) | RMSE (kW) | R2 | Time (s) |
---|---|---|---|---|
IVMD-FE-Ad-Informer | 83.01 | 60.43 | 0.962 | 1076.34 |
EMD-FE-Ad-Informer | 115.89 | 70.75 | 0.914 | 671.47 |
Ad-Informer | 144.51 | 105.46 | 0.866 | 156.91 |
LSTM | 186.63 | 131.04 | 0.762 | 228.88 |
ANN | 197.16 | 140.62 | 0.746 | 62.15 |
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Tian, Y.; Wang, D.; Zhou, G.; Wang, J.; Zhao, S.; Ni, Y. An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer. Entropy 2023, 25, 647. https://doi.org/10.3390/e25040647
Tian Y, Wang D, Zhou G, Wang J, Zhao S, Ni Y. An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer. Entropy. 2023; 25(4):647. https://doi.org/10.3390/e25040647
Chicago/Turabian StyleTian, Yuqian, Dazhi Wang, Guolin Zhou, Jiaxing Wang, Shuming Zhao, and Yongliang Ni. 2023. "An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer" Entropy 25, no. 4: 647. https://doi.org/10.3390/e25040647