Journal of the Korean Society for Geothermal and Hydrothermal Energy (한국지열·수열에너지학회논문집)

Korean Society for Geothermal and Hydrothermal Energy (한국지열·수열에너지학회)
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Heating Performance Prediction of Low-depth Modular Ground Heat Exchanger based on Artificial Neural Network Model

인공신경망 모델을 활용한 저심도 모듈러 지중열교환기의 난방성능 예측에 관한 연구

  • 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)
  • 오진환 (부산대학교 건축공학과) ;
  • 조정흠 (부산대학교 건축공학과) ;
  • 배상무 (부산대학교 생산기술연구소) ;
  • 채호병 (부산대학교 생산기술연구소) ;
  • 남유진 (부산대학교 건축공학과)
  • Received : 2022.07.25
  • Accepted : 2022.08.31
  • Published : 2022.09.01
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Abstract

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.

Keywords

Acknowledgement

이 과제는 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음.

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