The performance prediction of ground source heat pump system based on monitoring data and data mining technology.
发布时间：2023-04-22
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- 论文类型：
- 文章
- 第一作者：
- 严磊
- 通讯作者：
- 胡平放
- 合写作者：
- 李常虹,姚喻,邢路,雷飞,朱娜
- 发表刊物：
- Energy and Buildings
- 收录刊物：
- SCI
- 所属单位：
- 华中科技大学
- 刊物所在地：
- China
- 学科门类：
- 工学
- 一级学科：
- 土木工程
- 项目来源：
- 湖北省科技厅
- 文献类型：
- J
- 卷号：
- 127
- 页面范围：
- 1085-1095
- ISSN号：
- 0378-7788
- 关键字：
- GSHP system；Performance prediction；Data mining technology；Long-term；Short-term
- DOI码：
- 10.1016/j.enbuild.2016.06.055
- 发表时间：
- 2016-09-28
- 影响因子：
- 7.201
- 摘要：
- This paper studies the performance prediction of ground source heat pump (GSHP) systems by real-time monitoring data and data-driven models. A GSHP system, which is installed in an office building of Shaoxing (29.42 degrees N, 120.16 degrees E), China, is real-time monitored from Nov. 2012 to Mar. 2015. Data mining (DM) technologies were simultaneously applied to process the monitoring data and find the required inputs for data-driven models. Back-propagation Neural Network (BPNN) algorithm was selected from six classical sorting algorithms to establish the data-driven models. The performance of the GSHP system from Nov. 2012 to Mar. 2015 was evaluated by the monitoring data. And the long-term performance was predicted by the data-driven models. The monitoring results show that the application effectiveness of the GSHP system is unsatisfied because of the high pumping power. Moreover, the relationship between the short-term and long-term performance of GSHP system is investigated for the purpose of predicting the long-term performance of GSHP system by a short-term monitoring data. The monitoring data of different days in several modes are needed to predict the long-term performance of GSHP system under a certain deviation. (C) 2016 Elsevier B.V. All rights reserved.