Using genetic algorithm to develop a neural-network-based load forecasting

Authors:
Ronaldo R. B. de Aquino;Otoni Nóbrega Neto;Milde M. S. Lira;Aida A. Ferreira;Katyusco F. Santos
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 Federal Center of Technologic Education of Pernambuco, Recife, PE, Brazil
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 2
Cited 1

Combining artificial neural networks and heuristic rules in a hybrid intelligent load forecast system

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Abstract

This work uses artificial neural networks, whose architecture were developed using genetic algorithm to realize the hourly load forecasting based on the monthly total load consumption registered by the Energy Company of Pernambuco (CELPE). The proposed Hybrid Intelligent System - HIS was able to find the trade-off between forecast errors and network complexity. The load forecasting produces the essence to increase and strengthen in the basic grid, moreover study into program and planning of the system operation. The load forecasting quality contributes substantially to indicating more accurate consuming market, and making electrical system planning and operating more efficient. The forecast models developed comprise the period of 45 and 49 days ahead. Comparisons between the four models were achieved by using historical data from 2005.