An adaptive network based fuzzy inference system-fuzzy data envelopment analysis for gas consumption forecasting and analysis: The case of South America

This paper presents an adaptive-network-based fuzzy inference system (ANFIS)-fuzzy data envelopment analysis (FDEA)) for long-term natural gas (NG) consumption forecasting and analysis. Six models are proposed to forecast annual NG demand. 104 ANFIS have been constructed and tested in order to find the best ANFIS for natural gas (NG) consumption. Two parameters have been considered in construction and examination of plausible ANFIS models. Six different membership functions and several linguistic variables are considered in building ANFIS. The proposed models consist of two input variables, namely, Gross Domestic Product (GDP) and Population. All trained ANFIS are then 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 our outputs are post-processed (returned to its original scale). FDEA is used to examine the behavior of gas consumption. To show the applicability and superiority of the ANFIS-FDEA approach, actual gas consumption in six Southern America countries from 1980 to 2007 is considered. The gas consumption is then forecasted up to 2015. The ANFIS-FDEA approach is capable of dealing both complexity and uncertainty as well several other unique features discussed in this paper.