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Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data.

Ordo�ez, J.; Girard, A.; Simon, F.; Reddy, T.A.; Muneer, T.

Authors

J. Ordo�ez

A. Girard

F. Simon

T.A. Reddy



Abstract

The performance of ground-source heat pumps (GSHP), often expressed as Power drawn and/or the COP,
depends on several operating parameters. Manufacturers usually publish such data in tables for certain
discrete values of the operating fluid temperatures and flow rates conditions. In actual applications, such
as in dynamic simulations of heat pump system integrated to buildings, there is a need to determine
equipment performance under operating conditions other than those listed. This paper describes a
simplified methodology for predicting the performance of GSHPs using multiple regression (MR) models
as applicable to manufacturer data. We find that fitting second-order MR models with eight statistically
significant x-variables from 36 observations appropriately selected in the manufacturer catalogue can
predict the system global behavior with good accuracy. For the three studied GSHPs, the external prediction
error of the MR models identified following the methodology are 0.2%, 0.9% and 1% for heating
capacity (HC) predictions and 2.6%, 4.9% and 3.2% for COP predictions. No correlation is found between
residuals and the response, thus validating the models. The operational approach appears to be a reliable
tool to be integrated in dynamic simulation codes, as the method is applicable to any GSHP catalogue
data.

Journal Article Type Article
Acceptance Date Apr 19, 2016
Online Publication Date Apr 26, 2016
Publication Date 2016-09
Deposit Date May 5, 2016
Publicly Available Date Apr 27, 2017
Journal Renewable Energy
Print ISSN 0960-1481
Electronic ISSN 1879-0682
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 95
Pages 413-421
DOI https://doi.org/10.1016/j.renene.2016.04.045
Keywords GSHP (ground-source heat pump); Performance prediction;Manufacturer data; Multiple regression (MR);
Public URL http://researchrepository.napier.ac.uk/id/eprint/10050
Publisher URL http://dx.doi.org/10.1016/j.renene.2016.04.045
Contract Date May 5, 2016

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