[Behrang* et al., 5(7): July, 2016] ISSN: 2277-9655 IC™ Value: 3.00 Impact Factor: 4.116 http: // www.ijesrt.com © International Journal of Engineering Sciences & Research Technology [917] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY A NEW META-HEURISTIC FRAMEWORK FOR FORECASTING OIL DEMAND IN IRAN KH.Hemmatpour 1 , M.A.Behrang 1,2,* , E.Assareh 2 1 Farab Power Generation & Operation Management Co., Tehran, Iran. 2 Department of Mechanical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran. DOI: 10.5281/zenodo.57956 ABSTRACT 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 light- duty vehicles (LDVs). PGMH Model: Oil consumption is estimated based on population, GDP, export minus import, and number of heavy- duty 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. KEYWORDS: Cuckoo Search (CS) Algorithm; Oil; Projection; Demand; IRAN. INTRODUCTION The geostrategic situation of Iran and its access to the huge hydrocarbon resources placed the country among important areas and resulted in the investment development of oil and gas industry [1]. Iran, one of OPEC’s founding members, holds the world’s third-largest proven oil reserves and the world’s second- largest natural gas reserves [2, 3]. Iran's total recoverable oil reserves has increased due to recent discoveries and reached to around 138.22 billion barrels in 2006. This figure declares an increase about 2.1 billion barrels, something about 1.5% compared to its previous year [1]. In contrary to the public’s perception, Iran’s share of the market for high quality oil is as little as 2%.More specifically, the oil produced in Iran is ranked 14th in terms of the quality [3]. Oil industry plays a crucial role in Iran's economy, GDP, and government's annual budget. It is also influential in foreign trade, national capital, and developments in non-petroleum exports. For the Iranian government, it is also very important to effectively allocate oil revenues in the rest of its economy [1]. This study presents application of Cuckoo Search (CS) algorithm to forecast oil demand in Iran based on the Iran's socio-economic structure. Linear and non- linear forms of equations are developed. Eventually, In order to show the accuracy of the algorithm, a fair 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 consumption in Iran is forecasted up to year 2030.