Incorporating the effects of hike in energy prices into energy consumption forecasting: a fuzzy expert system

This paper proposes an adaptive fuzzy expert system to concurrently estimate and forecast both long-term electricity and natural gas (NG) consumptions with hike in prices. Using a novel procedure, the impact of price hike is incorporated into energy demand modeling. Furthermore, adaptive network-based FIS (ANFIS) is used to model NG consumption in power generation (NGPG). To cope with random uncertainty in small historical data sets, Monte Carlo simulation is used to generate training data for ANFIS. The proposed ANFIS uses electricity consumption data to improve the estimation of total NG consumption. The unique contribution of this paper is three fold. First, it proposes a novel expert system for electricity consumption and NG consumption in end-use sector with hike in prices. Second, it uses ANFIS-Monte Carlo approach for NGPG. Third, electricity consumption is used in ANFIS for improvement of NGPG consumption estimation. A real case study is presented that illustrates the applicability and usefulness of the proposed model where it is applied for joint forecasting of annual electricity and NG consumption with hike in prices.