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An Adaptive-Network-Based Fuzzy Inference System for Long-Term Electric Consumption Forecasting (2008-2015): A Case Study of the Group of Seven (G7) Industrialized Nations: U.S.A., Canada, Germany, United Kingdom, Japan, France and Italy

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4 Author(s)
Vahid Nadimi ; Dept. of Electr. Eng., Islamic Azad Univ. of Tafresh, Tafresh, Iran ; Ali Azadeh ; Peiman Pazhoheshfar ; Morteza Saberi

This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-term natural Electric consumption prediction. Six models are proposed to forecast annual Electric demand. 104 ANFIS have been constructed and tested in order to finding best ANFIS for Electric consumption. The proposed models consist of input variables such as Gross Domestic Product (GDP) and Population (POP). All of trained ANFIS are compared with respect to mean absolute percentage error (MAPE). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally outputs are post-processed (returned to its original scale). To show the applicability and superiority of the ANFIS, actual electric consumption are considered in industrialized nations including U.S.A, Canada, Germany, United Kingdom, Japan, France and Italy from 1980 to 2007. With aid of autoregressive model, GDP and population project by 2015 and then with yield value and best ANFIS model, Electric consumption predict by 2015.

Published in:

Computer Modeling and Simulation (EMS), 2010 Fourth UKSim European Symposium on

Date of Conference:

17-19 Nov. 2010