Training data sensitivity problem of artificial neural network-based power system load forecasting

摘要

A crucial problem with the artificial neural network-based load forecasting is that its forecasting performance is significantly affected by the selection of training data used to calculate the network weights. The inherent shortcoming of this approach is verified through a typical example presented in this paper. Test results show that the short-term load forecasting error is very sensitive to the amplitude of the noise signal which is added to a portion of the training data. The presented test cases approximately simulate the load conditions during abrupt weather changing periods. Possible strategies to remedy this problem are also discussed in the paper.