论文信息 - NeuroDem-a neural network based short term demand forecaster

NeuroDem-a neural network based short term demand forecaster

The application of Neural Network (NN) based short-term load forecasting (STLF) has developed to sophisticated practical systems over the years. However, the question of how to maximize the generalization ability of such machines, together with the choice of architecture, activation functions, training set data and size, etc. makes up a huge number of possible combinations for the final NN design, whose optimal solution has not been figured yet. This paper describes a STLF system (NeuroDem) which has been used by Brazilian electric utilities for 3 years. It uses a nonparametric model based on a linear model coupled with a polynomial network, identified by pruning/growing mechanisms. NeuroDem has special features of data pre-processing and confidence intervals calculations, which are also described. Results of load forecasts are presented for one year with forecasting horizons from 15 min to 168 hours ahead.

A.J.R. Reis | A.P.A. da Silva | U.P. Rodrigues | L.S. Moulin

[1] Hillol Kargupta,et al. System Identification with Evolving Polynomial Networks , 1991, ICGA.

[2] Mohamed A. El-Sharkawi,et al. Enhancing Neural Network Based Load Forecasting Via Preprocessing , 2001 .

[3] Andreas S. Weigend,et al. The Future of Time Series: Learning and Understanding , 1993 .

[4] L. S. Moulin,et al. Confidence intervals for neural network based short-term load forecasting , 2000 .

[5] Saifur Rahman,et al. Input variable selection for ANN-based short-term load forecasting , 1998 .

[6] Dirk Husmeier,et al. Predicting Conditional Probability Densities of Stationary Stochastic Time Series , 1997, Neural Networks.

[7] Alireza Khotanzad,et al. ANNSTLF-Artificial Neural Network Short-Term Load Forecaster- generation three , 1998 .

[8] James T. Kwok,et al. Constructive algorithms for structure learning in feedforward neural networks for regression problems , 1997, IEEE Trans. Neural Networks.