کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
765231 897027 2009 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی (عمومی)
پیش نمایش صفحه اول مقاله
Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks
چکیده انگلیسی

This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MRE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy Conversion and Management - Volume 50, Issue 1, January 2009, Pages 90–96
نویسندگان
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