• Land and Housing Review
• /
• v.10 no.3
• /
• pp.9-16
• /
• 2019

This study aimed at developing control algorithms for operating a variable refrigerant flow (VRF) heating and cooling system with optimal system parameter set-points. Two artificial neural network (ANN) models, which were respectively designed to predict the heating energy cost and cooling energy amount for upcoming next control cycle, was developed and embedded into the control algorithms. Performance of the algorithms were tested using the computer simulation programs - EnergyPlus, BCVTB, MATLAB in an incorporative manner. The results revealed that the proposed control algorithms remarkably saved the heating energy cost by as much as 7.93% and cooling energy consumption by as much as 28.44%, compared to a conventional control strategy. These findings support that the ANN-based predictive control algorithms showed potential for cost- and energy-effectiveness of VRF heating and cooling systems.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.25 no.3
• /
• pp.131-136
• /
• 2013

The present study has conducted cycle design and control technology of a water source VRF heat pump system. Previously, study of a simultaneous heating and cooling in an air source VRF heat pump system has been conducted. However, performance data and design methods for simultaneous heating and cooling in a water source VRF heat pump system are limited in the literature, due to various system parameters and operating conditions. In this study, the operating characteristics and performances of a simultaneous heating and cooling heat pump system are carried out, in simultaneous operation modes. Control logics of an EEV are developed for flow rate control to the outdoor unit, and are verified. When the control logics are applied, the simultaneous cooling and heating performances are sufficiently achieved, and system COPs are increased by up to 23.4%.