Title :
NeuroDem-a neural network based short term demand forecaster
Author :
Da Silva, A. P Alves ; Rodrigues, U.P. ; Reis, A. J Rocha ; Moulin, L.S.
Author_Institution :
Inst. of Electr. Eng., Fed. Eng. Sch. at Itajuba, Brazil
Abstract :
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
Keywords :
learning (artificial intelligence); load forecasting; neural nets; power consumption; power system analysis computing; 15 min to 168 hour; Brazil; NeuroDem; activation functions; architecture; confidence intervals calculations; data pre-processing; electric utilities; generalization ability; neural network; nonparametric model; polynomial network; power systems; pruning/growing mechanisms; short term demand forecaster; training set; Demand forecasting; Energy management; Engineering management; Input variables; Load forecasting; Neural networks; Power engineering and energy; Power industry; Predictive models; Systems engineering and theory;
Conference_Titel :
Power Tech Proceedings, 2001 IEEE Porto
Conference_Location :
Porto
Print_ISBN :
0-7803-7139-9
DOI :
10.1109/PTC.2001.964736
