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Indexed by:
Article
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First Author:
Lei Yan
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Correspondence Author:
Pingfang Hu
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Co-author:
Changhong Li,Yu Yao,Lu Xing,Fei Lei,Na Zhu
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Journal:
Energy and Buildings
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Included Journals:
SCI
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Affiliation of Author(s):
Huazhong University of Science and Technology
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Place of Publication:
China
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Discipline:
Engineering
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First-Level Discipline:
Civil Engineering
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Funded by:
Hubei Science and Technology Support Project
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Document Type:
J
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Volume:
127
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Page Number:
1085-1095
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ISSN No.:
0378-7788
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Key Words:
GSHP system；Performance prediction；Data mining technology；Long-term；Short-term
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DOI number:
10.1016/j.enbuild.2016.06.055
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Date of Publication:
2016-09-28
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Impact Factor:
7.201
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Abstract:
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.
