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

Moon, Jin Woo; Yang, Young Kwon; Choi, Young Jae; Kim, Yong Shik; Park, Bo Rang

doi:10.1016/j.applthermaleng.2018.12.044

Cited by 32 publications

(8 citation statements)

References 32 publications

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“…The development of a data-based model usually involves several variables with control rules [29]. However, installing expensive sensors must be minimized in order to improve field applicability.…”

Section: Methodsmentioning

confidence: 99%

Development of an Optimal Start Control Strategy for a Variable Refrigerant Flow (VRF) System

Lee

Kim

2021

Energies

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In this study, an optimal control strategy for the variable refrigerant flow (VRF) system is developed using a data-driven model and on-site data to save the building energy. Three data-based models are developed to improve the on-site applicability. The presented models are used to determine the length of time required to bring each zone from its current temperature to the set point. The existing data are used to evaluate and validated the predictive performance of three data-based models. Experiments are conducted using three outdoor units and eight indoor units on site. The experimental test is performed to validate the performance of proposed optimal control by comparing between conventional and optimal control methods. Then, the ability to save energy wasted for maintaining temperature after temperature reaches the set points is evaluated through the comparison of energy usage. Given these results, 30.5% of energy is saved on average for each outdoor unit and the proposed optimal control strategy makes the zones comfortable.

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Section: Methodsmentioning

confidence: 99%

Development of an Optimal Start Control Strategy for a Variable Refrigerant Flow (VRF) System

Lee

Kim

2021

Energies

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“…Some common control algorithms include proportional and integral (PI) 40,41 and proportional, integral, and derivative (PID), 42 fuzzy control, [43][44][45] multi-input and multioutput control (MIMO), 46,47 and artificial neural network model in the control algorithm. [48][49][50][51] For example, Chung et al 30 adopted an artificial neural network (ANN) model to predict the amount of cooling energy consumption for the different settings of the VRF cooling system's control variables. The optimized model demonstrated its prediction accuracy and proved its potential for application in the control algorithm for creating a comfortable indoor thermal environment.…”

Section: Introductionmentioning

confidence: 99%

“…Control algorithms are the technical means to achieve high EER and stable and reliable operation. Some common control algorithms include proportional and integral (PI) 40,41 and proportional, integral, and derivative (PID), 42 fuzzy control, 43–45 multi‐input and multioutput control (MIMO), 46,47 and artificial neural network model in the control algorithm 48–51 . For example, Chung et al 30 adopted an artificial neural network (ANN) model to predict the amount of cooling energy consumption for the different settings of the VRF cooling system's control variables.…”

Section: Introductionmentioning

confidence: 99%

Super‐heating degree responsive control strategy of variable refrigerant flow air conditioning system for energy saving and stable operation

Zhang

Yuan³

et al. 2023

Energy Science & Engineering

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The control strategy is important for variable refrigerant flow (VRF) air conditioning system to realize energy saving and stable operation. In this work, the super‐heating degree (SHD) responsive control strategy was developed to dynamically adjust the respective SHD target value (ΔTe,tar) of indoor units so as to adapt to the variable cooling load. The capacity code (Ecode) is introduced into the control model to quantify the reference target value of SHD. To responsively adapt to variable cooling load, the correction target values of SHD based on discharge temperature and deviation of the outlet air temperature are introduced in the SHD model for operation protection of the compressor and the consistency of refrigerant distribution. The enthalpy difference analogy method is developed to estimate the performance of the VRF system including cooling capacity, electric power, and energy efficiency ratio (EER). The performance was tested under the conditions of different ΔTe,tar. Experimental results demonstrate that the ΔTe,tar is an important factor to determine the operation frequency, which affects cooling capacity, power, EER, and operation status of the system. The cooling capacity and electric power at the ΔTe,tar of 9°C was about 55.2% and 63.5% lower than those at the ΔTe,tar of 1°C, and the corresponding EER was improved by 22.6%. To further verify the SHD control strategy, the VRF system was tested by the responsive control model under the condition that Ecode and ΔTe,tar were constantly automatically adjusted and changed with the actual indoor ambient temperature. Experimental results demonstrate that the VRF system can achieve a good and uniform cooling effect and realize energy saving and stable operation according to the responsive super‐heating degree control.

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“…The disadvantage of this method is overestimated specific heating characteristics, as a result of which, when calculating thermal loads according to the set of rules by designers [2], the data with the method for enlarged parameters of the object significantly differed [1]. This is justified by the introduction of new building materials for thermal protection of buildings, the use of automated individual heat points, new heating devices with higher heat transfer coefficients, the use of new materials for thermal protection of pipelines, as a result of which the consumption of thermal energy for heating buildings and reducing the specific heating characteristics of buildings are reduced [9][10][11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Development of engineering calculator for heating systems

Melekhin

2021

E3S Web Conf.

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The author has developed an engineering calculator for calculating the heat flow for heating buildings according to the enlarged parameters of the object. The algorithm of the calculator is based on the method of determining the amount of heat energy and heat carrier in water systems of municipal heat supply. The author carried out a systematic analysis of thermal loads on heating of buildings in Russia according to the data of implemented building projects. With this in mind, new coefficients a, n were calculated to determine the specific heating characteristics of the building for newly constructed buildings. The algorithm for calculating the heating of buildings according to the enlarged parameters of the object is corrected. The calculation algorithm is implemented in the software

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

Cited by 32 publications

References 32 publications

Development of an Optimal Start Control Strategy for a Variable Refrigerant Flow (VRF) System

Development of an Optimal Start Control Strategy for a Variable Refrigerant Flow (VRF) System

Super‐heating degree responsive control strategy of variable refrigerant flow air conditioning system for energy saving and stable operation

Development of engineering calculator for heating systems

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