کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
763935 1462871 2015 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Support vector regression based prediction of global solar radiation on a horizontal surface
ترجمه فارسی عنوان
بر اساس رگرسیون بردار مبتنی بر پیش بینی تابش خورشیدی جهانی بر روی سطح افقی
کلمات کلیدی
روش رگرسیون بردار پشتیبانی، برآورد جهانی تابش خورشیدی، ساعت آفتابی، حداکثر ساعت آفتاب ممکن است مدل های تجربی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی (عمومی)
چکیده انگلیسی
In this paper, the support vector regression (SVR) methodology was adopted to estimate the horizontal global solar radiation (HGSR) based upon sunshine hours (n) and maximum possible sunshine hours (N) as input parameters. The capability of two SVRs of radial basis function (rbf) and polynomial basis function (poly) was investigated and compared with the conventional sunshine duration-based empirical models. For this purpose, long-term measured data for a city situated in sunny part of Iran was utilized. Exploration was performed on both daily and monthly mean scales to accomplish a more complete analysis. Through a statistical comparative study, using 6 well-known statistical parameters, the results proved the superiority of developed SVR models over the empirical models. Also, SVR-rbf outperformed the SVR-poly in terms of accuracy. For SVR-rbf model on daily estimation, the mean absolute percentage error, mean absolute bias error, root mean square error, relative root mean square error and coefficient of determination were 10.4466%, 1.2524 MJ/m2, 2.0046 MJ/m2, 9.0343% and 0.9133, respectively. Also, on monthly mean estimation the values were 1.4078%, 0.2845 MJ/m2, 0.45044 MJ/m2, 2.2576% and 0.9949, respectively. The achieved results conclusively demonstrated that the SVR-rbf is highly qualified for HGSR estimation using n and N.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy Conversion and Management - Volume 91, February 2015, Pages 433-441
نویسندگان
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