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.

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

Most of steam power plant in Korea are heating the feed water system to prevent freezing water flowing in the pipe in winter time. The heating system is operated whenever the ambient temperature around the power plant area below 5 degree Centigrade. But this kind of heat supplying system cause a lot of energy consuming. If we think about the method that the temperature of the each pipe is controled by attaching the temperature measuring sensor like RTD sensor and heat is supplied only when the outer surface temperature of the pipe is under 5 degree Centigrade, then we can save a plenty of energy. In this study, the computer program package for simulation is used to compare the energy consumption load of both systems. Energy saving rate is calculated for the location of Youngweol area using the data of weather station in winter season, especially the January' severe weather data is analyzed for comparison. Various convection heat transfer coefficients for the ambient air and the flowing water inside the pipe was used for the accurate calculation. And also the various initial flowing water temperature was used for the system. Steady state analysis is done previously to approximate the result before the simulation. The result shows that the temperature control system using RTD sensor represents the high energy saving effect which is more than 90% of energy saving rate. Even in the severe January weather condition, the energy saving rate is almost 60%.