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An Integrated Intelligent Neuro-Fuzzy Algorithm for Long-Term Electricity Consumption: Cases of Selected EU Countries

2010; Óbuda University; Volume: 7; Issue: 4 Linguagem: Inglês

2064-2687

A. Azadeh, Morteza Saberi, Vahid Nadimi, Mino Iman, Alireza Behrooznia,

Solar Radiation and Photovoltaics

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 find the best ANFIS for Electric consumption. Two parameters have been considered in the construction and examination of plausible ANFIS models. The type of membership function and the number of linguistic variables are two mentioned parameters. Six different membership functions are considered in building ANFIS, as follows: the built-in membership function composed of the difference between two sigmoidal membership functions (dsig), the Gaussian combination membership function (gauss2), the Gaussian curve built-in membership function (gauss), the generalized bell-shaped built-in membership function (gbell), the Π-shaped built-in membership function (pi), psig. Also, a number for linguistic variables has been considered between 2 and 20. The proposed models consist of input variables such as: Gross Domestic Product (GDP) and Population (POP). Six distinct models based on different inputs are defined. All of the trained ANFIS are then compared with respect to the mean absolute percentage error (MAPE). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally our outputs are post-processed (returned to its original scale). The ANFIS model is capable of dealing with both complexity and uncertainty in the data set. To show the applicability and superiority of the ANFIS, the actual Electric consumption in industrialized nations including the Netherlands, Luxembourg, Ireland, and Italy from 1980 to 2007 are considered. With the aid of an autoregressive model, the GDP and population by 2015 is projected and then with yield value and best ANFIS model, Electric consumption by 2015 is predicted.