Fig 1.
Description of the short-term load forecasting problem.
Fig 2.
Strategy diagram of the short-term load forecasting method.
Fig 3.
Basic flowchart of Bagging.
Fig 4.
Basic network structure of SCNs.
Fig 5.
Basic flowchart of SCNs.
Fig 6.
Short-term load forecasting model of SCNs.
Fig 7.
Forecasting process of Bagging-SCNs.
Fig 8.
Time-dependent change of average load.
Fig 9.
Time-dependent change of average temperature.
Fig 10.
Forecasting process of Bagging-SCNs.
Fig 11.
Relationship between number of hidden nodes of SCNs and training error.
Table 1.
Number of hidden nodes of SCNs and the corresponding training error.
Fig 12.
Relationship between number of SCNs-based learners and error.
Table 2.
Number of SCNs-based learners and corresponding error.
Fig 13.
True value and predicted value of each method.
(a) The predicted results for June 15, 2018. (b) The predicted results for June 16, 2018. (c) The predicted results for December 7, 2018. (d) The predicted results for December 8, 2018.
Fig 14.
Actual value and predicted value of Bagging-SCNs.