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

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Anjan Rao Puttige & Staffan Andersson & Ronny Östin & Thomas Olofsson, 2021. "Application of Regression and ANN Models for Heat Pumps with Field Measurements," Energies, MDPI, vol. 14(6), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1750-:d:521763
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    References listed on IDEAS

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    3. Dhirendran Munith Kumar & Pietro Catrini & Antonio Piacentino & Maurizio Cirrincione, 2023. "Integrated Thermodynamic and Control Modeling of an Air-to-Water Heat Pump for Estimating Energy-Saving Potential and Flexibility in the Building Sector," Sustainability, MDPI, vol. 15(11), pages 1-23, May.

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