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

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.

Citation

Ordoñez, J., Girard, A., Simon, F., Reddy, T., & Muneer, T. (2016). Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data. Renewable Energy, 95, 413-421. https://doi.org/10.1016/j.renene.2016.04.045

• 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