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Information Journal Paper

Title

TRANSPORT ENERGY DEMAND FORECASTING USING NEURAL NETWORKS: CASE STUDY IRAN

Pages

  203-220

Abstract

 In this paper, the energy demand of transport sector from 1386 to 1400 was forecasted using artificial NEURAL NETWORKS (ANN) approach considering economic and social indicators. Feed forward supervised NEURAL NETWORKS to forecast and BACK PROPAGATION ALGORITHM to train networks were used. In order to analyze the influence of economic and social indicators on energy demand of transport sector, Gross Domestic Product (GDP), population and the total number ofvehic1es in 1347-1385were taken into consideration. The obtained results as compared with the multiple regression method, revealed much less mistakes. The average absolute error percentage was decreased from 15.52% to 6.05%.

Cites

References

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APA: Copy

MENHAJ, MOHAMMAD BAGHER, KAZEMI, ALIYEH, SHAKOURI GANJAVI, HAMED, MEHREGAN, MOHAMMAD REZA, & TAGHIZADEH, MOHAMMAD REZA. (2010). TRANSPORT ENERGY DEMAND FORECASTING USING NEURAL NETWORKS: CASE STUDY IRAN. MANAGEMENT RESEARCH IN IRAN (MODARES HUMAN SCIENCES), 14(2 (66)), 203-220. SID. https://sid.ir/paper/356545/en

Vancouver: Copy

MENHAJ MOHAMMAD BAGHER, KAZEMI ALIYEH, SHAKOURI GANJAVI HAMED, MEHREGAN MOHAMMAD REZA, TAGHIZADEH MOHAMMAD REZA. TRANSPORT ENERGY DEMAND FORECASTING USING NEURAL NETWORKS: CASE STUDY IRAN. MANAGEMENT RESEARCH IN IRAN (MODARES HUMAN SCIENCES)[Internet]. 2010;14(2 (66)):203-220. Available from: https://sid.ir/paper/356545/en

IEEE: Copy

MOHAMMAD BAGHER MENHAJ, ALIYEH KAZEMI, HAMED SHAKOURI GANJAVI, MOHAMMAD REZA MEHREGAN, and MOHAMMAD REZA TAGHIZADEH, “TRANSPORT ENERGY DEMAND FORECASTING USING NEURAL NETWORKS: CASE STUDY IRAN,” MANAGEMENT RESEARCH IN IRAN (MODARES HUMAN SCIENCES), vol. 14, no. 2 (66), pp. 203–220, 2010, [Online]. Available: https://sid.ir/paper/356545/en

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