OPTIMIZATION OF ECHO STATE NEURAL NETWORKS FOR ELECTRICAL LOAD FORECASTING

摘要

The predictive performance of Echo State neural networks were optimized for electrical load forecasting and compared to the results achieved by competitors in the worldwide Eunite Competition #1. The test data used were the actual results of the competition, attached to a specific region. A regular adaptation of an Echo State neural network was optimized by adapting the weights of the dynamic reservoir through Anti-Hebbian learning, and the weights from input and output neurons to the hidden neurons were optimized using the Metropolis algorithm. The results achieved with such an optimized Echo State neural network would gain a strong second place within the Eunite competition.