دانلود مقاله ISI انگلیسی شماره 52128
عنوان فارسی مقاله

طرح هوش محاسباتی برای پیش بینی اوج بار روزانه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52128 2011 16 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A computational intelligence scheme for the prediction of the daily peak load
منبع

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

Journal : Applied Soft Computing, Volume 11, Issue 8, December 2011, Pages 4773–4788

کلمات کلیدی
هوش محاسباتی؛ پیش بینی بار میان مدت ؛ اوج بار روزانه؛ نقشه خود سازمانده - ماشین بردار پشتیبان
پیش نمایش مقاله
پیش نمایش مقاله طرح هوش محاسباتی برای پیش بینی اوج بار روزانه

چکیده انگلیسی

Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to privatization and deregulation of the power industry, accurate electricity forecasting has become an important research area for efficient electricity production. This paper presents a time series approach for mid-term load forecasting (MTLF) in order to predict the daily peak load for the next month. The proposed method employs a computational intelligence scheme based on the self-organizing map (SOM) and support vector machine (SVM). According to the similarity degree of the time series load data, SOM is used as a clustering tool to cluster the training data into two subsets, using the Kohonen rule. As a novel machine learning technique, the support vector regression (SVR) is used to fit the testing data based on the clustered subsets, for predicting the daily peak load. Our proposed SOM-SVR load forecasting model is evaluated in MATLAB on the electricity load dataset provided by the Eastern Slovakian Electricity Corporation, which was used in the 2001 European Network on Intelligent Technologies (EUNITE) load forecasting competition. Power load data obtained from (i) Tenaga Nasional Berhad (TNB) for peninsular Malaysia and (ii) PJM for the eastern interconnection grid of the United States of America is used to benchmark the performance of our proposed model. Experimental results obtained indicate that our proposed SOM-SVR technique gives significantly good prediction accuracy for MTLF compared to previously researched findings using the EUNITE, Malaysian and PJM electricity load datasets.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.