Design of a multi-level fuzzy linear regression model for forecasting transport energy demand: A case study of Iran

Abstract

Linear regression has been used for many years for forecasting in marketing, management, sales and energy. In this paper, a fuzzy-based approach is applied for the transport energy demand forecasting using socio-economic and transport related indicators. This forecasting is analyzed based on gross domestic product (GDP), population and the number of vehicles together with historical energy data from 1993 to 2005. This approach is structured as a fuzzy linear regression (FLR). A multi-level FLR model is designed properly. The input variables are transport energy demand in the last year, the number of vehicles, population and ratio of GDP over population. The output variable is the energy demand of the transportation sector in Million Barrel Oil Equivalent (MBOE). This paper indeed proposes a multi-level FLR model by which the inputs to the ending level are obtained as outputs of the starting levels. Actual data from 1993-2005 is used to train and test the multi-level FLR and illustrate capability of the approach in this regard. The estimation fuzzy problem for the model is formulated as a linear optimization problem and is solved using the linear programming based simplex method. Comparison of the model predictions with data of the testing period shows validity of the proposed model. Furthermore, having obtained the fuzzy parameters, the transport energy demand is predicted from 2006 to 2020. It is noticeable that if there will not be any price shock or efficiency improvement in the transportation sector, the energy consumption may achieve a threatening level of about 592 MBOE per year by 2020.