Application of Regression and ANN Models for Heat Pumps with Field Measurements

Anjan Rao Puttige, Staffan Andersson, Ronny Östin and Thomas Olofsson
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Anjan Rao Puttige: Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden
Staffan Andersson: Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden
Ronny Östin: Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden
Thomas Olofsson: Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden

Energies, 2021, vol. 14, issue 6, 1-26

Abstract: Developing accurate models is necessary to optimize the operation of heating systems. A large number of field measurements from monitored heat pumps have made it possible to evaluate different heat pump models and improve their accuracy. This study used measured data from a heating system consisting of three heat pumps to compare five regression and two artificial neural network (ANN) models. The models’ performance was compared to determine which model was suitable during the design and operation stage by calibrating them using data provided by the manufacturer and the measured data. A method to refine the ANN model was also presented. The results indicate that simple regression models are more suitable when only manufacturers’ data are available, while ANN models are more suited to utilize a large amount of measured data. The method to refine the ANN model is effective at increasing the accuracy of the model. The refined models have a relative root mean square error (RMSE) of less than 5%.

Keywords: heat pump; artificial neural network; regression model; modeling; field measurements (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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