Research Article
Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network
Table 1
Results of empirical study in 2011.
| Number | Time | MAPE | RMSE | Number | Time | MAPE | RMSE |
| 1 | January 3 | 6.322 | 2.647 | 9 | August 9 | 3.095 | 2.191 | 2 | January 11 | 3.769 | 5.97 | 10 | August 16 | 3.616 | 2.094 | 3 | January 19 | 6.937 | 3.969 | 11 | August 25 | 2.31 | 5.967 | 4 | January 28 | 4.723 | 6.162 | 12 | August 31 | 3.202 | 1.987 | 5 | May 4 | 3.11 | 2.217 | 13 | October 8 | 2.459 | 5.967 | 6 | May 18 | 8.468 | 4.392 | 14 | October 19 | 4.193 | 2.228 | 7 | May 24 | 4.271 | 2.344 | 15 | October 22 | 2.719 | 5.969 | 8 | May 31 | 5.187 | 6.153 | 16 | October 28 | 3.616 | 2.092 |
| The mean of MAPE | 4.25% | The mean of RMSE | 3.897% |
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