...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Energy and Buildings >Critique of operating variables importance on chiller energy performance using random forest
【24h】

Critique of operating variables importance on chiller energy performance using random forest

机译：使用随机森林批判运行变量对冷水机组能源性能的重要性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Chiller systems take up the major proportion of electricity used in commercial buildings. Their energy performance in terms of coefficient of performance (COP) depends on how the controllable and uncontrollable variables change. The aim of this study is to use the random forest (RF) method to measure variables importance and predict the COP. A sophisticated data trend log was carried out on an air-cooled chiller with advanced heat rejection features. The variables measured are: the flow rate of chilled water; the supply and return temperatures of chilled water; the temperature and relative humidity of outdoor air; the compressor power; the evaporating temperature; the condensing temperature; the number and speed of condenser fans staged; the temperatures of air entering and leaving the condenser. The data were logged at 5-min intervals in Aug 2015-Mar 2016. The RF models for different operating modes were validated, with a robust coefficient of determination of 80.52-96.53% for the testing data set. The chiller part load ratio, the condensing temperature, the chilled water flow rate, the heat rejection airflow rate and the wet-bulb temperature are the top five important variables in the prediction of COP. Yet they are not fully considered in typical regression models. Results of this study provide an insight into which variables are important to predict accurately the COP under different energy efficient features. The need of identifying the changing pattern of important variables is ascertained. (C) 2017 Elsevier B.V. All rights reserved.
机译：冷却器系统占据了商业建筑中使用的大部分电能。以性能系数（COP）表示的能源性能取决于可控变量和不可控变量的变化方式。这项研究的目的是使用随机森林（RF）方法来测量变量的重要性并预测COP。在具有先进的散热功能的风冷式冷水机上进行了复杂的数据趋势记录。测量的变量为：冷冻水的流量；冷却水的供应和返回温度；室外空气的温度和相对湿度；压缩机功率；蒸发温度；冷凝温度冷凝器风扇的数量和速度；进入和离开冷凝器的空气温度。在2015年8月至2016年3月每隔5分钟记录一次数据。验证了不同操作模式的RF模型，测试数据集的确定系数为80.52-96.53％。冷水机部分负荷率，冷凝温度，冷水流量，排热风量和湿球温度是COP预测中的前五个重要变量。但是，在典型的回归模型中并未完全考虑它们。这项研究的结果为了解哪些变量对于准确预测不同能效特征下的COP至关重要。确定了确定重要变量的变化模式的需求。 （C）2017 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Energy and Buildings》 |2017年第3期|653-664|共12页
  • 作者

    Yu F. W.; Ho W. T.; Chan K. T.; Sit R. K. Y.;

  • 作者单位

    Hong Kong Polytech Univ, Hong Kong Community Coll, 8 Hung Lok Rd, Hong Kong, Hong Kong, Peoples R China;

    Hong Kong Polytech Univ, Hong Kong Community Coll, 8 Hung Lok Rd, Hong Kong, Hong Kong, Peoples R China;

    Hong Kong Polytech Univ, Dept Bldg Serv Engn, Hong Kong, Hong Kong, Peoples R China;

    Hong Kong Polytech Univ, Hong Kong Community Coll, 8 Hung Lok Rd, Hong Kong, Hong Kong, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Air-cooled chiller; Coefficient of performance; Heat rejection; Random forest;

    机译：风冷式冷水机组;性能系数;排热;随机森林;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号