Development of TRNSYS model for energy performance simulation of variable refrigerant flow air-conditioning system combined with energy recovery ventilation

ABSTRACT

The variable refrigerant flow air-conditioning system is an HVAC system that can perform at a high capacity with low consumption thanks to its variable-speed compressor. The paper introduces a TRNSYS model to build a simulation model to calculate the energy performance of an existing variable refrigerant flow system and to build a model that can be used as a reference when a VRF system is proposed to a certain situation. The TRNSYS model was compared with the actual energy consumption to see the data agreement between the two models. Temperature, Relative Humidity, and energy consumption were metered using proper instruments, and it was compared with the obtained simulation results. The validation of the simulation model showed close values to the actual system, as the obtained Normalized Mean Bias Error (NMBE) between the simulation and measured values for temperature and relative humidity was 4.3 and 7.6%, respectively. While it was around 7.8% for the total energy consumption. Besides, the paper proposed an Energy Recovery Ventilation to be simulated with the existing TRNSYS model to compare it with the same system without ventilation to see if it is feasible to add an Energy Recovery Ventilation unit to the existing system. It shows a worthy reason to be used, as it saves 18.8% annually of the energy consumption of the system.

KEYWORDS:

• Energy Recovery Ventilator
• variable Refrigerant Flow
• trnsys
• validation
• model Calibration
• building simulation

Acknowledgments

This research project was financially supported by the National Research, Development and Innovation Office from NRDI Fund [grant number: NKFIH PD_18 127907] and János Bolyai Research Scholarship of the Hungarian Academy of Sciences, Budapest, Hungary. Moreover, the research reported in this paper and carried out at the Budapest University of Technology and Economics was supported by the “TKP2020, National Challenges Program” of the National Research Development and Innovation Office (BME NC TKP2020), as well as by the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Artificial Intelligence research area of Budapest University of Technology and Economics (BME FIKP-MI). In addition, the authors acknowledge the Hungary Government for their financial support as the Stipendium Hungaricum Scholarship.

Nomenclature

• Air mass flow rate (kg/h)

• OAOutdoor Air

• EAExhaust air

• SASupply air

• RA Return Air

• RHRelative Humidity (%)

• TDry bulb temperature (^oC)

• $\dot{V}$Air volume flow rate (m³/h)

• VRF Variable Refrigerant Flow

• ERV Energy Recovery Ventilator

• ƮTime (hr)

Additional information

Funding

This work was supported by the National Challenges Program” of the National Research Development and Innovation Office [BME NC TKP2020]; National Research, Development and Innovation Office from NRDI [NKFIH PD_18 127907]; János Bolyai Research Scholarship of the Hungarian Academy of Sciences [BO/00025/18/6]; Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Artificial Intelligence research area of Budapest University of Technology and Economics [BME FIKP-MI]; National Research, Development and Innovation Fund, Thematic Excellence Program, Budapest, Hungary [TUDFO/51757/2019-ITM].