A Hybrid Intelligent System for Short and Mid-term Forecasting for the CELPE Distribution Utility

Author

De Aquino, Ronaldo R B ; Ferreira, Aida A. ; Lira, Milde M S ; Silva, Geane B. ; Neto, Otoni Nóbrega ; Oliveira, Josinaldo B. ; Diniz, Carlos F. ; Fideles, Juclar

Author_Institution

Federal Univ. of Pernambuco, Recife

fYear

0

fDate

0-0 0

Firstpage

2656

Lastpage

2661

Abstract

This paper presents the development of a hybrid intelligent system, joining an artificial neural network (ANN) based technique and heuristic rules to adjust the short and mid-term electric load forecasting in the 3, 7, 15, 30, and 45 days ahead. The study was based on load demand data of Energy Company of Pernambuco (CELPE), whose data contain the hourly load consumption in the period from January-2000 until December-2004. The proposed system forecasts a holiday as one Saturday or Sunday based on the specialist´s information that analyzes the load behaviors of each holiday. The hybrid intelligent system presented an improvement in the load forecasts in relation to the results achieved by the ANN alone. The program was implemented in MATLAB 7.0 R14.

Keywords

artificial intelligence; load forecasting; mathematics computing; neural nets; optimisation; power distribution planning; power engineering computing; Energy Company of Pernambuco distribution utility; MATLAB 7.0 R14; artificial neural network; electric load forecasting; heuristic rules; hybrid intelligent system; load demand; mid-term forecasting; short forecasting; time 15 day; time 3 day; time 30 day; time 45 day; time 7 day; Artificial neural networks; Hybrid intelligent systems; Information analysis; Load forecasting; MATLAB; Power industry; Power quality; Power system planning; Statistical analysis; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2006. IJCNN '06. International Joint Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9490-9

Type

conf

DOI

10.1109/IJCNN.2006.247145

Filename

1716455