Journal of the Korean Society of Fisheries and Ocean Technology (수산해양기술연구)

The Korean Society of Fisheries and Ocean Technology (한국수산해양기술학회)
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Power consumption prediction model based on artificial neural networks for seawater source heat pump system in recirculating aquaculture system fish farm

순환여과식 양식장 해수 열원 히트펌프 시스템의 전력 소비량 예측을 위한 인공 신경망 모델

  • Hyeon-Seok JEONG (Graduate School of Refrigeration and Air-conditioning Engineering, Pukyong National University) ;
  • Jong-Hyeok RYU (Graduate School of Refrigeration and Air-conditioning Engineering, Pukyong National University) ;
  • Seok-Kwon JEONG (Department of Refrigeration and Air-conditioning Engineering, Pukyong National University)
  • 정현석 (부경대학교 대학원 냉동공조공학과) ;
  • 류종혁 (부경대학교 대학원 냉동공조공학과) ;
  • 정석권 (부경대학교 냉동공조공학과)
  • Received : 2023.12.19
  • Accepted : 2024.02.27
  • Published : 2024.02.28
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Abstract

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

Keywords

Acknowledgement

이 논문은 2023년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No.2021R1I1A3049015).

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