دانلود مقاله ISI انگلیسی شماره 52367
عنوان فارسی مقاله

بهبود پیش بینی تقاضای انرژی با استفاده از هوش ازدحامی: مورد ترکیه با پیش بینی 2025

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52367 2008 8 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Energy Policy, Volume 36, Issue 6, June 2008, Pages 1937–1944

کلمات کلیدی
تقاضای انرژی؛ هوش ازدحامی - ترکیه
پیش نمایش مقاله
پیش نمایش مقاله بهبود پیش بینی تقاضای انرژی با استفاده از هوش ازدحامی: مورد ترکیه با پیش بینی 2025

چکیده انگلیسی

The energy supply and demand should be closely monitored and revised the forecasts to take account of the progress of liberalization, energy efficiency improvements, structural changes in industry and other major factors. Medium and long-term forecasting of energy demand, which is based on realistic indicators, is a prerequisite to become an industrialized country and to have high living standards. Energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. Energy demand management activities should bring the demand and supply closer to a perceived optimum. Turkey's energy demand has grown rapidly almost every year and is expected to continue growing. However, the energy demand forecasts prepared by the Turkey Ministry of Energy and Natural Resources overestimate the demand. Recently many studies are performed by researchers to forecast the energy demand of Turkey. Particle swarm optimization (PSO) technique has never been used for such a study. In this study a model is proposed, using PSO-based energy demand forecasting (PSOEDF), to forecast the energy demand of Turkey more efficiently. Although there are other indicators as well, gross domestic product (GDP), population, import and export are used as basic energy indicators of energy demand. In order to show the accuracy of the algorithm, a comparison is made with the ant colony optimization (ACO) energy demand estimation model which is developed for the same problem.

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