A mathematical modeling for incorporating energy price hikes into total natural gas consumption forecasting

In some countries that energy prices are low, price elasticity of demand may not be significant. In this case, large increase or hike in energy prices may impact energy consumption in a way which cannot be drawn from historical data. This paper proposes an integrated adaptive fuzzy inference system (FIS) to forecast long-term natural gas (NG) consumption when prices experience large increase. To incorporate the impact of price hike into modeling, a novel procedure for construction and adaptation of Takagi–Sugeno fuzzy inference system (TS-FIS) is suggested. Linear regressions are used to construct a first order TS-FIS. Furthermore, adaptive network-based FIS (ANFIS) is used to forecast NG consumption in power plants. To cope with random uncertainty in small historical data sets, Monte Carlo simulation is utilized to generate training data for ANFIS. To show the applicability and usefulness of the proposed model, it is applied for forecasting of annual NG consumption in Iran where removing energy subsidies has resulted in a hike in NG prices.