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

  • Type:


  • Date:

    26 April 2016

  • Publication Status:


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  • Library of Congress:

    TD Environmental technology. Sanitary engineering

  • Dewey Decimal Classification:

    621.47 Solar-energy enineering

  • Funders:

    UAI Earth Research Center; CONICYT/FONDAP


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.



GSHP (ground-source heat pump); Performance prediction;Manufacturer data; Multiple regression (MR);

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