کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6859743 1438733 2015 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines
ترجمه فارسی عنوان
پیش بینی مصرف برق: مقایسه ی تحلیل رگرسیون، شبکه های عصبی و کوچکترین مربعات از ماشین های بردار پشتیبانی می کند
کلمات کلیدی
پیش بینی مصرف برق، تجزیه و تحلیل رگرسیون، شبکه های عصبی مصنوعی، ماشین آلات بردار پشتیبانی از مربع حداقل،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Accurate electricity consumption forecast has primary importance in the energy planning of the developing countries. During the last decade several new techniques are being used for electricity consumption planning to accurately predict the future electricity consumption needs. Support vector machines (SVMs) and least squares support vector machines (LS-SVMs) are new techniques being adopted for energy consumption forecasting. In this study, the LS-SVM is implemented for the prediction of electricity energy consumption of Turkey. In addition to the traditional regression analysis and artificial neural networks (ANNs) are considered. In the models, gross electricity generation, installed capacity, total subscribership and population are used as independent variables using historical data from 1970 to 2009. Forecasting results are compared using diverse performance criteria in this study with each other. Receiver operating characteristic (ROC) analysis is realized for determining the specificity and sensitivity of the empirical results. The results indicate that the proposed LS-SVM model is an accurate and a quick prediction method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Electrical Power & Energy Systems - Volume 67, May 2015, Pages 431-438
نویسندگان
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