کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
494533 862799 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm
ترجمه فارسی عنوان
یک رویکرد جدید برای پیش بینی مصرف نفت اوپک بر اساس قطار شبکه عصبی با استفاده از الگوریتم گرده افشانی گل
کلمات کلیدی
الگوریتم گرده افشانی گل؛ شبکه عصبی؛ بهینه سازی ذرات سریع شتاب؛ سازمان کشورهای صادر کننده نفت (اوپک)؛ انرژی؛ مصرف نفت
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• We proposed a new forecasting method based on mete-heuristic algorithm.
• The method was applied to forecast OPEC petroleum consumption.
• The new method outperforms previous methods in forecasting OPEC petroleum consumption.
• The new method is an alternative means of forecasting OPEC petroleum consumption.

Petroleum is the live wire of modern technology and its operations, with economic development being positively linked to petroleum consumption. Many meta-heuristic algorithms have been proposed in literature for the optimization of Neural Network (NN) to build a forecasting model. In this paper, as an alternative to previous methods, we propose a new flower pollination algorithm with remarkable balance between consistency and exploration for NN training to build a model for the forecasting of petroleum consumption by the Organization of the Petroleum Exporting Countries (OPEC). The proposed approach is compared with established meta-heuristic algorithms. The results show that the new proposed method outperforms existing algorithms by advancing OPEC petroleum consumption forecast accuracy and convergence speed. Our proposed method has the potential to be used as an important tool in forecasting OPEC petroleum consumption to be used by OPEC authorities and other global oil-related organizations. This will facilitate proper monitoring and control of OPEC petroleum consumption.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 48, November 2016, Pages 50–58
نویسندگان
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