Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data
| Autor: | Javier Ordóñez, Tariq Muneer, Aymeric Girard, T A Reddy, François Simon |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Renewable Energy Sustainability and the Environment business.industry 020209 energy Mean squared prediction error Multiple regression (MR) 02 engineering and technology Transport Research Institute Reliability engineering Power (physics) law.invention Dynamic simulation law GSHP (ground-source heat pump) Linear regression TD Environmental technology. Sanitary engineering 0202 electrical engineering electronic engineering information engineering Operational approach Performance prediction 621.47 Solar-energy enineering business Simulation Performance predictionManufacturer data Heat pump |
| Zdroj: | Renewable Energy. 95:413-421 |
| ISSN: | 0960-1481 1879-0682 |
| DOI: | 10.1016/j.renene.2016.04.045 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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