Combined artificial neural network and adaptive neuro-fuzzy inference system for improving a short-term electric load forecasting

Authors:
Ronaldo R. B. de Aquino;Geane B. Silva;Milde M. S. Lira;Aida A. Ferreira;Manoel A. Carvalho, Jr.;Otoni Nóbrega Neto;Josinaldo B. de Oliveira
Affiliations:
Federal University of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil;Federal Federal Center of Technologic Education of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 2
Cited 1

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Abstract

The main topic in this work was the development of a hybrid intelligent system for the hourly load forecasting in a time period of 7 days ahead, using a combination of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. The hourly load forecasting was accomplished in two steps: in the first one, two ANNs are used to forecast the total load of the day, where one of the networks forecasts the working days (Monday through Friday), and the other forecasts the Saturdays, Sundays and public holidays; in the second step, the ANFIS was used to give the hourly consumption rate of the load. The proposed system presented a better performance as against the system currently used by Energy Company of Pernambuco, named PREVER. The simulation results showed an hourly mean absolute percentage error of 2.81% for the year 2005.