Abstract
This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal set-points for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for upcoming next control cycle, was embedded into the control algorithm. By comparing the predicted energy for the different set-point combinations of the control variables, the control algorithm can determine the most energy-effective set-points to optimally operate the cooling system. Two major processes were conducted in the development process. The first process was to develop the predictive control algorithm which embedded the ANN model. The second process involved performance tests of the control algorithm in terms of prediction accuracy and energy efficiency in computer simulation programs. The results revealed that the prediction accuracy between simulated and predicted outcomes proved to have a low coefficient of variation root mean square error (CVRMSE) value (10.30%). In addition, the predictive control algorithm markedly saved the cooling energy consumption by as much as 28.44%, compared to a conventional control strategy. These findings suggest that the ANN model and the control algorithm showed potential for the prediction accuracy and energy-effectiveness of VRF cooling systems.
Highlights
The amount of energy consumed worldwide has been increasing, causing serious environmental problems, such as global warming, urban heat island phenomenon, resource depletion, and air pollution [1]
The aim of this study is to develop a control algorithm capable of operating the variable refrigerant flow (VRF) cooling systems in an energy efficient manner by setting control variables that are optimally determined by an artificial neural network (ANN) model
The findings revealed that the ANN model trained and 2 = 0.94) based on the data of the five previous days
Summary
The amount of energy consumed worldwide has been increasing, causing serious environmental problems, such as global warming, urban heat island phenomenon, resource depletion, and air pollution [1]. The Korean government has suggested many policies and established various institutions for energy reduction, focusing on the building sector, in which the proportion of energy use is about 24% of the total national energy consumption [4,5]. Since the building sector accounts for a large portion of total energy consumption and has a relatively higher potential to reduce energy than other sectors, this could be an effective strategy in reducing energy consumption [6,7]. Energies 2018, 11, 1643 and has a relatively higher potential to reduce energy than other sectors, this could be an effective strategy in reducing energy consumption [6,7].
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