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Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster

Author

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  • Fredrik Skaug Fadnes

    (Department of Energy and Petroleum, University of Stavanger, 4021 Stavanger, Norway
    Department of Energy and Smart Technology, Norconsult AS, 1338 Sandvika, Norway)

  • Reyhaneh Banihabib

    (Department of Energy and Petroleum, University of Stavanger, 4021 Stavanger, Norway)

  • Mohsen Assadi

    (Department of Energy and Petroleum, University of Stavanger, 4021 Stavanger, Norway)

Abstract

The use of heat pumps for heating and cooling of buildings is increasing, offering an efficient and eco-friendly thermal energy supply. However, their complexity and system integration require attention to detail, and minor design or operational errors can significantly impact a project’s success. Therefore, it is essential to have a thorough understanding of the system’s intricacies and demands, specifically detailed system knowledge and precise models. In this article, we propose a method using artificial neural networks to develop heat pump models from measured data. The investigation focuses on an operational heat pump plant for heating and cooling a cluster of municipal buildings in Stavanger, Norway. The work showcases that the network configurations can provide process insights and knowledge when detailed system information is unavailable. Model A predicts the heat pump response to temperature setpoint and inlet conditions. Except for some challenges during low-demand cooling mode, the model predicts outlet temperatures with Mean Absolute Percentage Error (MAPE) between 2 and 5% and energy production and consumption with MAPE below 10%. Summarizing the five-minute interval predictions, the model predicts the hourly energy production and consumption with MAPE at 3% or less. Model B predicts energy consumption and coefficient of performance (COP) from measured inlet and outlet conditions with MAPE below 5%. The model may serve as a tool to develop system-specific compressor maps for part-load conditions and for real-time performance monitoring.

Suggested Citation

  • Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3875-:d:1138554
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    References listed on IDEAS

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