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The Modeling and Comparison of GMDH and RBF Artificial Neural Networks in Forecasting Consumption of Petroleum Products in the Agricultural Sector | ||
| Iranian Journal of Economic Studies | ||
| مقاله 6، دوره 8، شماره 1 - شماره پیاپی 15، شهریور 2019، صفحه 91-105 اصل مقاله (602.27 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/ijes.2018.29049.1444 | ||
| نویسندگان | ||
| Mojtaba Abbasian1؛ Ali Sardar Shahraki* 2؛ Javad shahraki2 | ||
| 1Faculty of Economic, Chabahar University, Chabahar, Iran | ||
| 2Department of Economic, University of Sistsn and Baluchestan, Zahedan,Iran | ||
| چکیده | ||
| Energy plays a significant role in today's developing societies. The role of energy demands to make decisions and policy with regard to its production, distribution, and supply. The vital importance of energy, especially fossil fuels, is a factor affecting agricultural production. This factor has a great influence on the production of agricultural products in Iran. The forecast of the consumption of oil products by the agricultural sector can help managers and planners to adopt sound management practices for their consumption. Presently, artificial neural networks are regarded as a powerful tool for the analysis and modeling of nonlinear relationships. The present study employed GMDH and RBF artificial neural networks to estimate the consumption of oil products by the agricultural sector. The underpinning parameters were selected to include the value added to the fixed price, rural population, agricultural land area, agricultural mechanization (tractor), and the consumption rate of oil products, electricity, price of oil products, and total energy use by the agricultural sector for the period of 1967-2017. The comparison of MSE, MAE, and MAPE for the GMDH and RBF models showed that the GMDH neural network was highly capable of modeling the energy consumption of the agricultural sector. | ||
| کلیدواژهها | ||
| Artificial neural network؛ Oil؛ Radial basis function (RBF)؛ Group method of data handling؛ Agricultural sector | ||
| مراجع | ||
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References
Abbasi, E. (2015). Prediction of energy consumption by the agricultural sector of Iran. Quarterly Journal of Financial Economics, 9 (32), 81-102. (In Persian) Alam, M. S., Alam, M. R. & Islam, K. K. (2005). Energy flow in agriculture: Bangladesh, American Journal of Environmental Science, vol. 3, 213-220. Atashkari, K., Nariman-Zadeh, N., Gölcü, M., Khalkhali, A. & Jamali, A. (2007) Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms; Energy Conversion and Management, Vol. 48, Issue 3, 1029-1041. Barthelmie, R. J., Murray, F., & Pryor, S. C. (2008). The economic benefit of short-term forecasting for wind energy in the UK electricity market. Energy Policy, 36(5), 1687-1696. Ebrahimi, M. (2012). Use of artificial neural network (ANN) and time series approach for prediction of electricity consumption in agricultural sector. Journal of Agricultural Economics Research, 4 (13), 27-42. (In Persian with English Abstract) Ghasemi, A. (2012). An overview of the evolution of energy economy indicators in the agricultural sector, the monthly review of issues and economic policies, 3: 169-184. (In Persian) Haykin, S. (1999). Neural networks: a comprehensive foundation, Prentice Hall, New Jersey, USA. Ivakhnenko, A. G., & Muller J. A. (1995). Present state and new problems of further GMDH development,System Analysis Modeling and Simulation; (SAMS), Vol. 20, No. 1-2, 3-16. Ivakhnenko, A. G., & Müller, J. A. (1995). Recent Developments of Self-Organising Modeling in Prediction and Analysis of Stock Market, < https://pdfs.semanticscholar.org/be26/7e3a9c2843cd756a4ef029a295104225ae0c.pdf > Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, 431-438. Lee, Y. S., & Tong, L. I. (2012). Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Applied Energy, 94, 251-256. Li, K., & Su, H. (2010). Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system. Energy and buildings, 42(11), 2070-2076. Menhaj, M., Kazemi, A., Shakuri Ghanjwari, H., Mehrgan, M., & Taghizadeh, M. (2010). Transport energy demand forecasting using neural networks: Case study Iran. Management Research in Iran, 14 (2), 203-220. (In Persian with English Abstract) Mousavi, S., Mokhtari, Z., & Farajpour, Z. (2010). Prediction of energy carriers consumption rate by the agricultural sector of Iran: The application of ARCH and ARIMA models. Quarterly Journal of Energy Economics Review, 7 (27), 181-195. (In Persian) Nelles, O. (2001). Nonlinear system identification, Springer Verlag, Berlin. Pukšec, T., Krajačić, G., Lulić, Z., Mathiesen, B. V., & Duić, N. (2013). Forecasting long-term energy demand of Croatian transport sector. Energy, 57, 169-176. Sadeghi, H., Afzalian, A., Haghani, M., &Sohrabivafa, H. (2013). Forecasting the long run electricity demand using hybrid PSO-ANFIS algorithm. Journal of Economic Modeling Research, 3 (10), 21-56. (In Persian with English Abstract) Sardar Shahraki, A. (2017). Optimal allocation of Hearmand's water resources resources using game theory and evaluation of management scenarios. PhD Thesis, Agricultural Economics University of Sistan and Baluchestan, Zahedan, Iran Taghizadeh Mehrjerdi, R., Fatahi Ardakani, A., Tahari, M.H., & Babaie, H. (2015). Prediction of Iran's agricultural energy consumption using the combined model of genetic algorithm and artificial neural networks, Agricultural Economics Research, 3: 149-166. (In Persian) Taheri, F., & Mousavi, S. (2010). Analyzing the role of energy in the Iranian agricultural sector. Journal of Agricultural Economics Research, 2 (6), 45-60. (In Persian with English Abstract) Wang, X.,Li, K.,Li, H.,Bai, D., & Liu, J. (2017). Research on China’s rural household energy consumption–Household investigation of typical counties in 8 economic zones. Renewable and Sustainable Energy Reviews, 68, 28-32.
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