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Heating Performance Prediction of Low-depth Modular Ground Heat Exchanger based on Artificial Neural Network Model  

Oh, Jinhwan (Department of Architecture Engineering, Pusan National University)
Cho, Jeong-Heum (Department of Architecture Engineering, Pusan National University)
Bae, Sangmu (Research Institute of Industrial Technology, Pusan National University)
Chae, Hobyung (Research Institute of Industrial Technology, Pusan National University)
Nam, Yujin (Department of Architecture Engineering Pusan National University)
Publication Information
Journal of the Korean Society for Geothermal and Hydrothermal Energy / v.18, no.3, 2022 , pp. 1-6 More about this Journal
Ground source heat pump (GSHP) system is highly efficient and environment-friendly and supplies heating, cooling and hot water to buildings. For an optimal design of the GSHP system, the ground thermal properties should be determined to estimate the heat exchange rate between ground and borehole heat exchangers (BHE) and the system performance during long-term operating periods. However, the process increases the initial cost and construction period, which causes the system to be hindered in distribution. On the other hand, much research has been applied to the artificial neural network (ANN) to solve problems based on data efficiently and stably. This research proposes the predictive performance model utilizing ANN considering local characteristics and weather data for the predictive performance model. The ANN model predicts the entering water temperature (EWT) from the GHEs to the heat pump for the modular GHEs, which were developed to reduce the cost and spatial disadvantages of the vertical-type GHEs. As a result, the temperature error between the data and predicted results was 3.52%. The proposed approach was validated to predict the system performance and EWT of the GSHP system.
Artificial neural network; Modular ground heat exchanger; Real-scale experiment; Heating performance;
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