An integrated GA-time series algorithm for forecasting oil production estimation: USA, Russia, India, and Brazil

This study presents an integrated algorithm for forecasting oil production based on a Genetic Algorithm (GA) with variable parameters using stochastic procedures, time series and Analysis of Variance (ANOVA). The significance of the proposed algorithm is two fold. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted GA model based on Minimum Absolute Percentage Error (MAPE) or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future oil production forecasting because of its dynamic structure, whereas previous studies assume that GA always provides the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the data for oil production in USA, Russia, India and Brazil from 2001 to 2006 are used and applied to the proposed algorithm.

[1]  OLCAY ERSEL CANYURT,et al.  Energy Demand Estimation Based on Two-Different Genetic Algorithm Approaches , 2004 .

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

[3]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[4]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[5]  Adnan Sözen,et al.  Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies , 2007 .

[6]  B. Kermanshahi,et al.  Up to year 2020 load forecasting using neural nets , 2002 .

[7]  László Monostori,et al.  Engineering of Intelligent Systems , 2001, Lecture Notes in Computer Science.

[8]  G. T. S. Ho,et al.  A fuzzy logic approach to forecast energy consumption change in a manufacturing system , 2008, Expert Syst. Appl..

[9]  Guoqiang Peter Zhang,et al.  Neural network forecasting for seasonal and trend time series , 2005, Eur. J. Oper. Res..

[10]  Michael Ye,et al.  Forecasting short-run crude oil price using high- and low-inventory variables , 2006 .

[11]  S. F. Ghaderi,et al.  Integration of Artificial Neural Networks and Genetic Algorithm to Predict Electrical Energy consumption , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[12]  P. C. Nayak,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[13]  Fang-Mei Tseng,et al.  Combining neural network model with seasonal time series ARIMA model , 2002 .

[14]  Dulakshi S. K. Karunasinghe,et al.  Chaotic time series prediction with a global model: Artificial neural network , 2006 .

[15]  N. Hatziargyriou,et al.  A non-linear multivariable regression model for midterm energy forecasting of power systems , 2007 .

[16]  Seyed Taghi Akhavan Niaki,et al.  A genetic algorithm approach to find the best regression/econometric model among the candidates , 2006, Appl. Math. Comput..

[17]  Mahdi Nasereddin,et al.  Forecasting output using oil prices: A cascaded artificial neural network approach , 2006 .

[18]  Mahmoud A. Abo-Sinna,et al.  A combined genetic algorithm-fuzzy logic controller (GA-FLC) in nonlinear programming , 2005, Appl. Math. Comput..

[19]  Hiok Chai Quek,et al.  GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms , 2005, Expert Syst. Appl..

[20]  A. Gil,et al.  Forecasting of electricity prices with neural networks , 2006 .

[21]  R. E. Abdel-Aal,et al.  Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks , 2008, Comput. Ind. Eng..

[22]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[23]  Ali Azadeh,et al.  INTEGRATION OF GENETIC ALGORITHM, COMPUTER SIMULATION AND DESIGN OF EXPERIMENTS FOR FORECASTING ELECTRICAL ENERGY CONSUMPTION , 2007 .

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

[25]  Sam Mirmirani,et al.  A Comparison of VAR and Neural Networks with Genetic Algorithm in Forecasting Price of Oil , 2003, IC-AI.