Solving the Carbon Dioxide Emission Estimation Problem: An Artificial Neural Network Model
- 1 Computer Science Department, Ajloun College, Al-Balqa Applied University, Ajloun, Jordan
Abstract
Climate Pollution due to the Carbon Emission (CO 2 ) from the different fossil fuels is considered as a great and impor tant international challenge to many researchers. In this paper we are providing a solution to forecast the poison CO 2 gas emerged from energy consumption. Four inputs data were considered the global oil, natural gas, coal, and primary energy consumption to build our system. In this paper , we used the Artificial Neural Network (ANN) as successful and powerful tool in handling a time series modeling problem. The proposed ANN model was used to train and test the yearly CO 2 Emission. The data were trained from year 1982 to 2000, and tested for the year 2003 to 2010. From the results obtained we can see that ANN performance was Excellent and proved its efficiency as a useful tool in solving the climate pollution problems.
- M. R. Lotfalipour, M. A. Falahi and M. Ashena, “Economic Growth, CO2 Emissions, and Fossil Fuels Consumption in Iran,” Energy Policy, Vol. 2010, No. 35, 2010, pp. 5115-5120.
- H. Davoudpour and M. S. Ahadi, “The Potential for Greenhouse Gases Mitigation in Household Sector of Iran: Cases of Price Reform/Efficiency Improvement and Scenario for 2000-2010,” Energy Policy, Vol. 34, No. 1, 2006, pp. 40-49.
- I. A. Samoilov, A. I. Nakhutin, “Esimation and Meddium-Term Forecasting of Anthropogenic Carbon Dioxide and Methods Emission in Russia with Statistical Methods,” Vol. 34, No. 6, 2009, pp. 348-353.
- W. David, “Reduction in Carbon Dioxide Emissions: Estimating the Potential Contribution from Wind Power,” Renewable Energy Foundation, December 2004.
- M. A. Behrang,. E. Assareh, M. R. Assari and A. Ghanbarzadeh, “Using Bees Algorithm and Artificial Neural Network to Forecast World Carbon Dioxide Emission,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, Vol. 33, No. 19, 2011, pp. 1747-1759.
- H. T. Pao and C. M. Tsai, “Modeling and Forecasting the CO2 Emissions, Energy Consumption, and Economic Growth in Brazil,” Energy, Vol. 36, No. 5, 2011, pp. 2450-2458.
- N. Karunanithi, W. Grenney, D. Whitley and K. Bovee, “Neural Networks for River Flow Prediction,” Journal of Computing in Civil Engg, Vol. 8, No. 2, 1993, pp. 371-379.
- P. R. Bulando and J. Salas, “Forecasting of Short-Term Rainfall Using ARMA Models,” Journal of Hydrology, Vol. 144, No. 1-4, 1993, pp. 193-211.
- H. Hruschka, “Determining Market Response Functions by Neural Networks Modeling: A Comparison to Econometric Techniques,” European Journal of Operational Research, Vol. 66, 1993, pp. 867-888.
- E. Y. Li, “Artificial Neural Networks and Their Business Applications,” Information and Managements, Vol. 27, No. 5, 1994, pp. 303-313.
- K. Chakraborty, “Forecasting the Behavior of Multivariable Time Series Using Neural Networks,” Neural Networks, Vol. 5, 1992, pp. 962-970.
- G. Swales and Y. Yoon, “Applying Artificial Neural Networks to Investment Analysis,” Financial Analyst Journal, Vol. 48, No. 5, 1992, pp. 78-82.
- M. Negnevissky, “Artificial Intelligence: A Guide to Intelligent Systems,” 2nd Edition, Addison-Wesley, Boston, 2005.
- E. Y. Li, “Artificial Neural Networks and Their Business Applications,” Information and Managements, Vol. 27, No. 5, 1994, pp. 303-313.