Support vector regression modeling of the performance of an R1234yf automotive air conditioning system

被引：15

作者：

Hosoz, Murat ^{[1
]}

Kaplan, Kaplan ^{[2
]}

Aral, M. Celil ^{[1
]}

Suhermanto, Mukhamad ^{[3
]}

Ertunc, H. Metin ^{[2
]}

机构：

[1] Kocaeli Univ, Dept Automot Engn, TR-41380 Kocaeli, Turkey

[2] Kocaeli Univ, Dept Mechatron Engn, TR-41380 Kocaeli, Turkey

[3] State Univ Malang, Dept Mech Engn, Malang 65145, Indonesia

来源：

5TH INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENT RESEARCH (ICEER 2018) | 2018年 / 153卷

关键词：

Air conditioning; R1234yf; support vector regression; SVR; AAC; ARTIFICIAL NEURAL-NETWORKS;

D O I：

10.1016/j.egypro.2018.10.067

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This study aims at modelling various performance parameters of an automotive air conditioning (AAC) system using support vector regression (SVR), a novel soft modelling technique. For this purpose, a bench-top AAC system was set up, charged with alternative refrigerant R1234yf, and tested in a wide range of operating conditions. Next, the cooling capacity and coefficient of performance of the AAC system were evaluated. Then, the proposed SVR was trained by using some of the input-output data pairs, and the performance of model predictions was tested using the remaining data. It was determined that the SVR model yielded very accurate predictions. (C) 2018 The Authors. Published by Elsevier Ltd.

引用

页码：309 / 314

页数：6

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