Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models
Abstract
:1. Introduction
2. RF-Based Input Variables Selection
2.1. Basic Principle of the RF Method
2.2. Measuring Feature Importance Based on Out-of-Bag Prediction Accuracy
2.3. MDA-Based Input Variable Selection
3. Construction of a Wind Speed Forecasting Model Based on Input Variable Selection
3.1. Candidate Input Variable Selection
3.2. KELM Modelling and GA Optimization
3.3. Forecasting Results Evaluation
4. Case Study
4.1. Data Source and Parameter Initialization
4.2. Candidate Input Variable Selection
4.3. Feature Selection Based on the RF Method
4.4. KELM-Based Modelling and Parameter Optimization
4.5. Forecasting Results and Model Comparisons
5. Conclusions
Author Contributions
Conflicts of Interest
References
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Variable | Matrix | Meaning | Dimension | Total Dimension |
---|---|---|---|---|
Input | 8 | 32 | ||
8 | ||||
8 | ||||
8 | ||||
Output | 4 | 4 |
Variable | Matrix | Meaning | Dimension | Total Dimension |
---|---|---|---|---|
Input | 2 | 7 | ||
- | 0 | |||
2 | ||||
3 | ||||
Output | 4 | 4 |
Model | Configuration Details |
---|---|
RBF | Transfer function: Gaussian, spread of RBF: 1. |
NN | Sizes of hidden layers: 5, transfer function: tansig. Parameters by GA: initial weights and thresholds. |
SVM | Transfer function: Gaussian RBF. Parameters by GA: width of kernel, penalty coefficient. |
ELM | Number of hidden neurons: 20, transfer function: sigmoidal. Parameters by GA: weights of input layer, bias of hidden layer. |
Model | MAE (m/s) | MAPE (%) | RMSE (m/s) |
---|---|---|---|
Persistence | 1.1782 | 21.83 | 1.1693 |
WT-RBF | 1.3169 | 22.05 | 1.7152 |
WT-RF-RBF | 1.0568 | 19.76 | 1.3803 |
WT-NN-GA | 1.4018 | 23.36 | 1.6628 |
WT-RF-NN-GA | 0.7373 | 13.80 | 0.9776 |
WT-SVM-GA | 1.1319 | 21.09 | 1.5676 |
WT-RF-SVM-GA | 0.7598 | 13.55 | 1.0289 |
WT-ELM-GA | 1.2156 | 21.98 | 1.5857 |
WT-RF-ELM-GA | 0.7688 | 13.83 | 1.0350 |
WT-KELM-GA | 1.1694 | 21.54 | 1.5303 |
WT-RF-KELM-GA | 0.7047 | 12.54 | 0.9518 |
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Wang, H.; Sun, J.; Sun, J.; Wang, J. Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models. Energies 2017, 10, 1522. https://doi.org/10.3390/en10101522
Wang H, Sun J, Sun J, Wang J. Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models. Energies. 2017; 10(10):1522. https://doi.org/10.3390/en10101522
Chicago/Turabian StyleWang, Hui, Jingxuan Sun, Jianbo Sun, and Jilong Wang. 2017. "Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models" Energies 10, no. 10: 1522. https://doi.org/10.3390/en10101522