Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data. (September 2016)

• Record Type: Journal Article
• Authors: Simon, F.; Ordoñez, J.; Reddy, T.A.; Girard, A.; Muneer, T.
• Is Part Of: Renewable energy. Volume 95(2016)
• Page Start: 413
• Page End: 421
• Publication Date: 2016-09
• Subjects: GSHP (ground-source heat pump) -- Performance prediction -- Manufacturer data -- Multiple regression (MR); Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2016.04.045
• Languages: English
• ISSNs: 0960-1481

Abstract

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. Highlights: A method for the performance prediction of GSHPs based on MR modeling is presented. The operational approach for the identification of MR models from manufacturer data tables is statistically validated. The proposed mathematical models are reliable tools to be integrated in dynamic simulation codes.