Development of a control algorithm aiming at cost-effective operation of a VRF heating system

• Published in: Applied thermal engineering Vol. 149; pp. 1522 - 1531
• Main Authors: Moon, Jin Woo, Yang, Young Kwon, Choi, Eun Ji, Choi, Young Jae, Lee, Kwang-Ho, Kim, Yong-Shik, Park, Bo Rang
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 25.02.2019; Elsevier BV
• Subjects: Accuracy; Air temperature; Algorithms; Artificial neural network; Artificial neural networks; Coefficient of variation; Computer simulation; Computing time; Control algorithms; Control theory; Cost analysis; Energy conservation; Energy cost; Energy costs; Heating; Heuristic methods; Mathematical models; Parameter estimation; Performance tests; Predictive control; Predictive controls; Refrigerants; Simulation; Thermal energy; Variable refrigerant flow heating system; Energy cost; Artificial neural network; Predictive controls; Variable refrigerant flow heating system
• ISSN: 1359-4311; 1873-5606
• DOI: 10.1016/j.applthermaleng.2018.12.044

•Cost-effective algorithm was developed for the control of VRF heating system.•ANN model was embedded in the algorithm to determine the cost-effective operation.•ANN stably predicted the cost for space heating.•Proposed algorithm saved the heating cost compared to the conventional method. This study aims to develop a control algorithm that can operate an intermittently working variable refrigerant flow (VRF) heating system in a cost-effective manner. An artificial neural network (ANN) model, which is designed to predict the heating energy cost during the next control cycle, is embedded in the control algorithm. By comparing the predicted energy costs for the different setpoint combinations for the system parameters, such as the air handling unit (AHU) supply air temperature, condensing warm fluid temperature, condensing warm fluid amount, and refrigerant condensing temperature, the control algorithm can determine the most cost-effective setpoints to optimally operate the heating system. Two major processes are conducted—development of the predictive control algorithm in which the ANN model is embedded, and performance tests in terms of prediction accuracy and cost efficiency using computer simulation. Results analysis reveals that the ANN model accurately predicts the energy cost, presenting a low coefficient of variation of the root mean square error value (7.42%) between the simulated and predicted results. In addition, the predictive control algorithm significantly saves on the heating energy cost by as much as 7.93% compared with the conventional heuristic control method. From the results analysis, the ANN model and the control algorithm show the potential for prediction accuracy and cost-effectiveness of the intermittently working VRF heating system.