دانلود مقاله ISI انگلیسی شماره 57360
عنوان فارسی مقاله

پیش بینی قیمت و اعتبار در بازار برق با استفاده از پیش بینی های اسپانیایی به عنوان داده های ورودی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
57360 2016 5 صفحه PDF سفارش دهید 3230 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Price forecasting and validation in the Spanish electricity market using forecasts as input data
منبع

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

Journal : International Journal of Electrical Power & Energy Systems, Volume 77, May 2016, Pages 123–127

کلمات کلیدی
شبکه های عصبی مصنوعی؛ بازار برق؛ پیش بینی قیمت؛ مدل رگرسیون؛ نوسانات
پیش نمایش مقاله
پیش نمایش مقاله پیش بینی قیمت و اعتبار در بازار برق با استفاده از پیش بینی های اسپانیایی به عنوان داده های ورودی

چکیده انگلیسی

The electricity sector has been subjected to major changes in the last few years. Previously, there existed a regulated system where electric companies could know beforehand the amount of energy each generator would produce, hence basing their largely operational strategy on cost minimization in order to increase their profits. In Spain, from 1988 till 1997, electricity prices were established by the ‘Marco Legal Estable’ – Stable Legal Framework –, where the Ministry of Industry and Energy acknowledged the existence of certain generation costs related to each type of technology. It was an industrial sector with no actual competition and therefore, with very few controllable risks. In the aftermath of the electricity market liberalization competition and uncertainty arose. Electricity spot prices became highly volatile due to the specific characteristics of electricity as a commodity. Long-term contracts allowed for hedge funds to act against price fluctuation in the electricity market. As a consequence, developing an accurate electricity price forecasting model is an extremely difficult task for electricity market agents. This work aims to propose a methodology to improve the limitations of those methodologies just using historical data to forecast electricity prices. In this manner, and in order to gain access to more recent data, instead of using natural gas prices and electricity load historical data, a regression model to forecast the evolution of natural gas prices, and a model based on artificial neural networks (ANN) to forecast electricity loads, are proposed. The results of these models are used as input for an electricity price forecast model. Finally, and to demonstrate the effectiveness of the proposed methodology, several study cases applied to the Spanish market, using real price data, are presented.

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