A NEW META-HEURISTIC FRAMEWORK FOR FORECASTING OIL DEMAND IN IRAN

Energy is a central element to achieve the interrelated economic, social, and environmental goals toward sustainable development of each country. Detailed, complete, timely and reliable statistics are essential to monitor the energy situation and develop energy demand estimation models at a country as well as at international level to make sound energy policy decisions. In this study, a novel approach for oil consumption modeling is presented. For this purpose, following demand estimation models are developed using Cuckoo Search (CS) algorithm to forecast oil consumption: PGIE Model: Oil consumption is estimated based on population, GDP, import and export. PGML Model: Oil consumption is estimated based on Population, GDP, export minus import, and number of lightduty vehicles (LDVs). PGMH Model: Oil consumption is estimated based on population, GDP, export minus import, and number of heavyduty vehicles (HDVs). Linear and non- linear forms of equations are developed for each model. Eventually, In order to show the accuracy of the CS algorithm, a comparison is made with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) estimation models which are developed for the same problem. Oil demand in Iran is forecasted up to year 2030.