Short-Term Wind Speed Prediction Study Based on Variational Mode Decompositions–Sparrow Search Algorithm–Gated Recurrent Units
Abstract
:1. Introduction
2. Establishment of Wind Speed Prediction Models
2.1. Overall Framework
2.2. VMD
2.3. GRU Neural Network Model
2.4. Sparrow Optimization Algorithm
2.5. Adaptive VMD–SSA–GRU Model
2.6. SSA Performance Test
3. Wind Speed Prediction Evaluation
3.1. Model Evaluation Metrics
3.2. Original Wind Speed
3.3. Wind Speed Decomposition
3.4. Single Model Prediction Performance
3.5. Hybrid Model Predictive Performance
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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First Classification | Second Classification | Ref. |
---|---|---|
Signal model | CNN | [5] |
LSTM | [6] | |
BiLSTM | [7] | |
GRU | [8] | |
TCN | [9] | |
Transformer | [10] | |
Hybrid model | WP-CNN-LSTM | [11] |
WT-DeepAR | [12] | |
EEMD-LSTM | [13] | |
VMD-CNN-GRU | [14] |
Type | Number | Function | fmin |
---|---|---|---|
Unimodal Functions | F1 | 0 | |
F2 | 0 | ||
Unimodal Functions | F3 | −2.09 × 103 | |
Fixed Dimension | F4 | 1 | |
F5 | 3 | ||
F6 | −10.5363 |
Number | Index | SSA | WOA | TSO | BES | BWO |
---|---|---|---|---|---|---|
F1 | Ave | 3.85 × 10−169 | 1.84 × 10−169 | 0.00 | 0.00 | 0.00 |
Std | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
F2 | Ave | 2.93 × 10−91 | 2.66 × 10 | 1.52 × 10−4 | 3.15 × 10−3 | 5.23 × 10−255 |
Std | 1.60 × 10−90 | 3.77 × 10−1 | 4.28 × 10−4 | 4.22 × 10−3 | 0.00 | |
F3 | Ave | −2.67 × 103 | −1.20 × 104 | −1.26 × 104 | −8.16 × 103 | 3.30 × 10−5 |
Std | 2.66 × 10−4 | 9.25 × 102 | 1.38 × 10−5 | 1.24 × 103 | 2.56 × 10−5 | |
F4 | Ave | 9.98 × 10−1 | 1.56 | 9.98 × 10−1 | 3.80 | 4.44 × 10−16 |
Std | 0.00 | 1.85 | 1.91 × 10−16 | 4.59 | 0.00 | |
F5 | Ave | 3.00 | 3.00 | 3.00 | 3.00 | 1.64 × 10−25 |
Std | 1.24 × 10−15 | 1.14 × 10−6 | 1.19 × 10−15 | 1.26 × 10−15 | 2.32 × 10−25 | |
F6 | Ave | −1.05 × 10 | −9.43 | −1.05 × 10 | −8.91 | −1.04 × 10 |
Std | 8.73 × 10−16 | 2.34 | 2.09 × 10−15 | 2.52 | 3.17 × 10−6 |
Prediction Model | BP | CNN | SVM | LSTM | GRU |
---|---|---|---|---|---|
RMSE/(m·s−1) | 0.36953 | 0.41718 | 0.36951 | 0.36797 | 0.3679 |
MAE/(m·s−1) | 0.28301 | 0.32404 | 0.27949 | 0.28185 | 0.28109 |
MAPE/% | 10.8521 | 12.5183 | 10.9059 | 10.9466 | 10.8986 |
Hyperparameters | Optimization Range | Optimal Value |
---|---|---|
GRU Network Layers | [20, 400] | 244 |
Learning Rate | [0.0001, 0.2] | 0.00198 |
Maximum Iterations | [20, 500] | 338 |
Hyperparameters | Optimization Range | Optimal Value |
---|---|---|
LSTM Network Layers | [20, 400] | 308 |
Learning Rate | [0.0001, 0.2] | 0.00115 |
Maximum Iterations | [20, 500] | 269 |
Models | RMSE/(m·s−1) | MAE/(m·s−1) | MAPE/% |
---|---|---|---|
WT–GRU | 0.26098 | 0.20602 | 7.6101 |
WT–SSA–GRU | 0.22181 | 0.17218 | 6.5966 |
WT–LSTM | 0.27153 | 0.21287 | 8.0096 |
WT–SSA–LSTM | 0.22679 | 0.17691 | 6.6146 |
VMD–GRU | 0.25171 | 0.20403 | 7.5058 |
VMD–SSA–GRU | 0.10018 | 0.080954 | 3.1461 |
VMD–LSTM | 0.26179 | 0.20469 | 7.9107 |
VMD–SSA–LSTM | 0.13428 | 0.087361 | 3.2432 |
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Yang, T.; Guo, X.; Qian, G. Short-Term Wind Speed Prediction Study Based on Variational Mode Decompositions–Sparrow Search Algorithm–Gated Recurrent Units. Processes 2024, 12, 1741. https://doi.org/10.3390/pr12081741
Yang T, Guo X, Qian G. Short-Term Wind Speed Prediction Study Based on Variational Mode Decompositions–Sparrow Search Algorithm–Gated Recurrent Units. Processes. 2024; 12(8):1741. https://doi.org/10.3390/pr12081741
Chicago/Turabian StyleYang, Tongrui, Xihao Guo, and Guowei Qian. 2024. "Short-Term Wind Speed Prediction Study Based on Variational Mode Decompositions–Sparrow Search Algorithm–Gated Recurrent Units" Processes 12, no. 8: 1741. https://doi.org/10.3390/pr12081741