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.

This study deals with the maximum thermal load analysis and optimal capacity determination method of tank culture system for applying seawater source heat pump to save energy and realize zero energy. The location of the fish farm was divided into four sea areas, and the heat load in summer and winter was analyzed, respectively. In addition, two representative methods, the flow-through aquaculture system and the recirculation aquaculture system were reviewed as water treatment methods for fish farms. In addition, the concept of the exchange rate was introduced to obtain the maximum heat load of the fish farms. Finally, power consumption for heat pumps was analyzed in the view point of sea areas, tank capacity, and exchange rate based on the calculated maximum thermal load.