Estimating gasoline demand in Iran using different soft computing techniques

Present study develops two scenarios to analyse gasoline consumption and makes future projections based on the particle swarm optimisation (PSO) and genetic algorithm (GA). The gasoline consumption is estimated based on the basic indicators of the population, gross domestic product (GDP), import, export, gasoline production and number of cars figures. Two different exponential and linear estimation models are developed for each scenario using PSO and GA methods. Developed models are validated with actual data, while future estimation of gasoline demand is projected between 2006 and 2030. For the best result (PSO - PGIE{inexponential), the relative error average was 1.03%.

[1]  I-Tung Yang,et al.  Performing complex project crashing analysis with aid of particle swarm optimization algorithm , 2007 .

[2]  Andries Petrus Engelbrecht,et al.  Locating multiple optima using particle swarm optimization , 2007, Appl. Math. Comput..

[3]  V. Ediger,et al.  Forecasting the primary energy demand in Turkey and analysis of cyclic patterns , 2002 .

[4]  A. Hepbasli,et al.  Electricity estimation using genetic algorithm approach: a case study of Turkey , 2005 .

[5]  Zbigniew Michalewicz,et al.  Adaptation in evolutionary computation: a survey , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[6]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[7]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[8]  M. Duran Toksarı,et al.  Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey , 2009 .

[9]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[11]  V. Ediger,et al.  ARIMA forecasting of primary energy demand by fuel in Turkey , 2007 .

[12]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  Hong Liu,et al.  Particle Swarm Optimization based on Dynamic Niche Technology with Applications to Conceptual Design , 2006, 2006 10th International Conference on Computer Supported Cooperative Work in Design.

[14]  Adnan Sözen,et al.  Modelling of Turkey's net energy consumption using artificial neural network , 2005, Int. J. Comput. Appl. Technol..

[15]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[16]  Ünal Çamdali,et al.  Energy and exergy efficiencies in Turkish transportation sector, 1988-2004 , 2007 .

[17]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .

[18]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[19]  John J. Grefenstette,et al.  How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.

[20]  Arif Hepbasli,et al.  Estimating Energy and Exergy Production and Consumption Values Using Three Different Genetic Algorithm Approaches. Part 2: Application and Scenarios , 2005 .

[21]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[22]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[23]  Zülal Güngör,et al.  A fuzzy outranking method in energy policy planning , 2000, Fuzzy Sets Syst..

[24]  Zbigniew Michalewicz,et al.  A Hierarchy of Evolution Programs: An Experimental Study , 1993, Evolutionary Computation.

[25]  Thomas Bäck,et al.  Optimal Mutation Rates in Genetic Search , 1993, ICGA.

[26]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[27]  Adnan Sözen,et al.  Prospects for Future Projections of the Basic Energy Sources in Turkey , 2007 .

[28]  Ling Wang,et al.  Particle swarm optimization for function optimization in noisy environment , 2006, Appl. Math. Comput..

[29]  Abdolreza Karbassi,et al.  Sustainability of energy production and use in Iran , 2007 .

[30]  Bernard Gilland.  Population economic growth and energy demand 1985-2020. , 1988 .

[31]  Carol Dahl,et al.  Forecasting Energy Demand in the Developing World , 1998 .

[32]  Diyar Akay,et al.  Grey prediction with rolling mechanism for electricity demand forecasting of Turkey , 2007 .

[33]  H. Jafari,et al.  The Crisis of Gasoline Consumption in the Iran's Transportation Sector , 2008 .

[34]  Ayhan Demirbas,et al.  Energy balance, energy sources, energy policy, future developments and energy investments in Turkey , 2001 .

[35]  Z. Yumurtacı,et al.  Electric Energy Demand of Turkey for the Year 2050 , 2004 .

[36]  M. Toksari.  Ant colony optimization approach to estimate energy demand of Turkey , 2007 .

[37]  Benjamin F. Hobbs,et al.  Artificial neural networks for short-term energy forecasting: Accuracy and economic value , 1998, Neurocomputing.

[38]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[39]  Lothar Thiele,et al.  A Comparison of Selection Schemes used in Genetic Algorithms , 1995 .

[40]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[41]  Soner Haldenbilen,et al.  Genetic algorithm approach to estimate transport energy demand in Turkey , 2005 .

[42]  Y. Işık.  Turkey ' s Energy Prospects in the EU-Turkey Context EU-Turkey Working Paper No . 9 / October 2004 , 2004 .

[43]  Harun Kemal Ozturk,et al.  Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach , 2004 .

[44]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.