FORECAST CONSUMPTION ENERGY OF IRAN USING HYBRID MODEL OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS AND COMPARED WITH TRADITIONAL METHODS

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MIRFAKHRADINI SEYED HEYDAR; BABAEI MEYBODI HAMID; MOROVATI SHARIFABADI ALI

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Journal: Management Research in Iran Year:2013 | Volume:17 | Issue:2 (79) Page(s): 196-222

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Title

FORECAST CONSUMPTION ENERGY OF IRAN USING HYBRID MODEL OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS AND COMPARED WITH TRADITIONAL METHODS

Author(s)

MIRFAKHRADINI SEYED HEYDAR | BABAEI MEYBODI HAMID | MOROVATI SHARIFABADI ALI | Issue Writer Certificate

Keywords

CONSUMPTION ENERGYQ1

ARTIFICIAL NEURAL NETWORKSQ2

GENETIC ALGORITHMSQ2

Abstract

During recent decades, Energy as one of the most important factors of production and also as one of the most important end products, a special place in the country's economic development and growth development. Hence, the country authorities should try to predict anything more precise energy consumption in the proper planning and guidance consumption, to control the way they desired energy demand and supply parameters. The purpose of this paper is Evaluation Hybrid model of ARTIFICIAL NEURAL NETWORKS and GENETIC ALGORITHMS in the forecast CONSUMPTION ENERGY of Iran. Therefore in this study, data from the annual energy consumption as the output forecasting model range and was used as input variables, data of the annual total population, GDP, imports and exports.The end results were assessed with of different models (RSE), (ME) and (RMSE). Evaluation results showed that the hybrid model of neural networks and genetic algorithm (ANN-GA), compared to other models with the highest accuracy in predicting CONSUMPTION ENERGY of Iran.

Cites

SELECTING THE APPROPRIATE SCENARIO TO PREDICT THE ENERGY DEMAND OF RESIDENTIAL AND COMMERCIAL SECTORS USING PARTICLE SWARM OPTIMIZATION

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APA: Copy

MIRFAKHRADINI, SEYED HEYDAR, BABAEI MEYBODI, HAMID, & MOROVATI SHARIFABADI, ALI. (2013). FORECAST CONSUMPTION ENERGY OF IRAN USING HYBRID MODEL OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS AND COMPARED WITH TRADITIONAL METHODS. MANAGEMENT RESEARCH IN IRAN (MODARES HUMAN SCIENCES), 17(2 (79)), 196-222. SID. https://sid.ir/paper/398650/en

Vancouver: Copy

MIRFAKHRADINI SEYED HEYDAR, BABAEI MEYBODI HAMID, MOROVATI SHARIFABADI ALI. FORECAST CONSUMPTION ENERGY OF IRAN USING HYBRID MODEL OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS AND COMPARED WITH TRADITIONAL METHODS. MANAGEMENT RESEARCH IN IRAN (MODARES HUMAN SCIENCES)[Internet]. 2013;17(2 (79)):196-222. Available from: https://sid.ir/paper/398650/en

IEEE: Copy

SEYED HEYDAR MIRFAKHRADINI, HAMID BABAEI MEYBODI, and ALI MOROVATI SHARIFABADI, “FORECAST CONSUMPTION ENERGY OF IRAN USING HYBRID MODEL OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS AND COMPARED WITH TRADITIONAL METHODS,” MANAGEMENT RESEARCH IN IRAN (MODARES HUMAN SCIENCES), vol. 17, no. 2 (79), pp. 196–222, 2013, [Online]. Available: https://sid.ir/paper/398650/en

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