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.
