Research Article
A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks
Table 12
Performance results of prediction methods.
| Method | Corresponding number | MSE | R | MAE | Time (s) |
| Linear | 1 | 9.90e-02 | 0.75 | 0.20 | 2.10 | Interactions Linear | 2 | 9.39e-02 | 0.77 | 0.20 | 4.79 | Robust Linear | 3 | 1.05e-01 | 0.74 | 0.19 | 3.74 | Stepwise Linear | 4 | 9.40e-02 | 0.77 | 0.20 | 1256.50 | Fine Tree | 5 | 1.30e-01 | 0.68 | 0.22 | 7.09 | Medium Tree | 6 | 1.09e-01 | 0.73 | 0.20 | 3.53 | Coarse Tree | 7 | 1.01e-01 | 0.75 | 0.19 | 2.73 | Linear SVM | 8 | 1.03e-01 | 0.75 | 0.19 | 96.97 | Quadratic SVM | 9 | 9.78e-02 | 0.76 | 0.19 | 214.74 | Cubic SVM | 10 | 9.25e-02 | 0.77 | 0.18 | 828.17 | Fine Gaussian SVM | 11 | 2.07e-01 | 0.49 | 0.29 | 159.63 | Medium Gaussian SVM | 12 | 9.05e-02 | 0.78 | 0.18 | 112.76 | Coarse Gaussian SVM | 13 | 9.88e-02 | 0.76 | 0.19 | 78.12 | Boosted Trees | 14 | 9.39e-02 | 0.77 | 0.19 | 23.57 | Bagged Trees | 15 | 8.45e-02 | 0.79 | 0.17 | 54.57 | BNN_16 | 16 | 8.26e-02 | 0.89 | 0.18 | 9.33 |
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