Variable Refrigerant Flow System Research Articles

Machine learning based diagnosis strategy for refrigerant charge amount malfunction of variable refrigerant flow system

Malfunctions would occur in a variable refrigerant flow (VRF) system after years of operation or inappropriate maintenance, thus causing unnecessary energy waste and even occupant discomfort. This study presents a machine learning based malfunction diagnosis strategy that combines the recursive feature elimination algorithm (RFE) and the classification algorithms for the typical malfunctions of VRF system. RFE based on Random Forest (RF) model firstly serves as the feature selection process to evaluate variables importance, thus acquiring the key variables related to malfunction. Then five kinds of machine learning classification models are trained using the chosen key variables to diagnosis refrigerant leakage malfunction. By comparison, the AdaBoost.M1 (ABM) model shows the most desirable performance on the all nine malfunction severity levels. The results show that the RFR-RF based feature selection method can select the most six critical variables and the ABM model established based on the six variables achieves admirable diagnostic accuracy and AUC value for faults corresponding to nine severity levels.

Performance based thermal comfort control (PTCC) using deep reinforcement learning for space cooling

With the recent increase in energy consumption in buildings, energy-saving strategies in buildings have become a priority in the energy policies of many countries. Therefore, many recent research studies have emphasized the advanced control methods to attain comfortable thermal conditions while minimizing the energy consumption in buildings. A new approach of thermal comfort control for space cooling/heating system is needed to reflect the changing indoor environment information in real time, and to control various factors (e.g., humidity, air velocity, etc.) that affect not only the temperature but also the thermal comfort.In this study, we propose the Gaussian process regression (GPR) for real-time thermal comfort prediction, a data-driven approach. These data-driven approaches will enable the monitoring of occupants and thermal comfort conditions based on real-time data and situational awareness. Then, based on the thermal comfort performance (PMV) prediction results obtained using the GPR, we investigated control methods involving the integration of systems, i.e., a variable refrigerant flow (VRF) system and a humidifier, instead of using simple set-temperature control for space cooling. For this purpose, deep Q-learning, which is an reinforcement learning method, was employed to derive the VRF and humidification integrated control methods. During zone operation, this algorithm learned an effective control policy based on rewards (thermal comfort and energy consumption) without relying on a thermal dynamics model. Moreover, by comparing the thermal comfort and energy consumption results with those obtained using fixed set-point (rule-based) control and performance-based comfort control for cooling, the efficiency of the proposed performance-based thermal comfort control (PTCC) was evaluated.As a results, it was found that PTCC yielded the optimal control action value that minimized the energy consumption while satisfying the thermal comfort conditions. In addition, applying the proposed PTCC strategy to cooling control could maintain the required performance level of thermal comfort by reflecting changing environmental conditions in real time, unlike the fixed set-point control.

Experimental analysis of a solar thermal hybrid VRF system for maximum energy economy based on Delhi (India) climate

ABSTRACT The main objective of the present study is to develop a solar thermal hybrid variable refrigerant flow (VRF) air-conditioning system and predict energy saving in this system compared to normal VRF air-conditioning system. In R410a based VRF system compressor works partially to increase pressure and temperature up to 27 bar, 55°C from compressor suction pressure and temperature 27 bar, 55°C. Further increase in temperature and pressure from approximate 55°C, 27 bar (Solar heat exchanger inlet) to 80°C, 34.39 bar (Solar heat exchanger outlet) is achieved with the help of evacuated tube type solar collector-based heat exchanger. The calculation of energy saving in solar thermal hybrid VRF system has been compared with fully electric-fired VRF system. The energy saving of approximate 45–50% can be achieved in solar thermal hybrid VRF system as compared to normal VRF system.

Energy use analysis of the variable refrigerant flow (VRF) system versus the multi - split unit using TRNSYS

The usage of air - conditioning in the residential and commercial buildings is becoming necessary because of the enormous demand for thermal comfort and healthy indoor environment. In achieving human thermal comfort and healthy indoor environment in a large building, there are many types of air conditioning systems that can be used, including the multi - split type unit and the variable refrigerant flow (VRF) system. The present research analyses the annual energy use of the existing ACMV system (multi - split type ceiling cassette unit) installed in the examined building, which located in the tropical area and compares it with a VRF system. The TRNSYS software is used to simulate both of these systems in accordance with the existing building’s characteristics and its operation. The existing building’s characteristics and its operation are obtained by conducting the site survey and field measurements in the building. In the current paper, the bin method is used to estimate the annual energy consumption for these systems. Based on the simulation results, the existing ACMV system consumes more energy in comparison to the VRF system by 13.62%. In addition, the annual operating cost of these systems has shown that the VRF system has a lower annual operating cost in comparison to the existing ACMV system by 13.63%. Besides that, the VRF system can accommodate an estimated payback period of around 6.6 years and an internal rate of return of 12.75%. The result has suggested that the VRF system has a great potential in energy savings and could reduce the electricity consumption in a large building significantly.

Performance of VRF systems based on large scale monitoring

Variable Refrigerant Flow (VRF) systems are refrigerant systems, which are generally comprised of an outdoor unit serving multiple indoor units connected by a refrigerant piping network. It is important to evaluate the performance of VRF systems, which can help the design and operate of VRF systems. Performance test done by manufactory can reveal the performance of VRF systems in designed conditions. However, it is hard to reveal effective performance in real buildings. The field test is complicated compared with the test in the laboratory and can only conduct on typical samples rather than large scale samples. However, typical samples are not enough for reflecting the performance of large scale VRF systems samples. A simple method of evaluating the performance of large scale VRF systems samples is necessary. This paper proposed and calculating model for electricity consumption and cooling demand of VRF systems based on measured operating data in the laboratory. The paper used the calculating model combined with 344 samples operating data from real residential buildings to calculate the performance of a large scale VRF. This paper analyzed the VRF systems’ performance with different influencing factors such as climate zones, cooling duration and outdoor temperature for the recommendation for VRF systems’ designing and operation.

An electronic expansion valve modeling framework development using artificial neural network: A case study on VRF systems

Electronic expansion valves (EEV) are widely used in variable refrigerant flow systems (VRF) to control the mass flow rate of each indoor unit. EEV model is used to predict the mass flow rate through an EEV. While the power-law correlation method has been used to build the EEV model so far, Artificial Neural Network (ANN) methods have been adapted to model the EEV with a fixed speed compressor thanks to its higher accuracy. However, the EEV is typically working with the variable speed compressor in a VRF system. In addition, the parameters used in ANN modeling could be further optimized. The objective of this study is to develop an EEV modeling framework and optimize the input parameter number and hidden neuron number. We presented the framework through an EEV model development for a VRF system. For these, we used the field test data, applied a principal components analysis approach in optimizing the ANN input parameter number, and investigated the proper number of hidden neurons and appropriate transfer function pairs. We found the performance of the ANN model would not improve much as the number of input parameters and the number of hidden neurons reached a threshold. Only three transfer function pairs are suitable for this case in total nine groups we studied. Our ANN model has the average absolute deviation of 2.2%. The framework can be easily applied to EEV modeling in other VRF and air condition systems with necessary data support.

Annual energy performance simulation of solar chimney in a cold winter and hot summer climate

The paper studies the energy performance of a solar chimney (SC) in a high performance two-story detached house with 220 m2 floor area using EnergyPlus and the climate data of the hot summer and cold winter in China. An 8 m tall and 1.6 m wide solar chimney with a depth of 1 m is attached to the west wall to enhance building ventilation. The house uses a variable refrigerant flow (VRF) system to provide the heating and cooling and a separate ventilation system for outdoor air. The energy simulation results are compared between the house with the SC (SC case) and the same house but without SC (reference case). The results show that the SC produces larger ventilation rates than the minimum required rate most time of the year and therefore it needs to be controlled to avoid excessive outdoor air that leads to increased heating/cooling loads during the heating/cooling seasons. In this paper, a control is assumed so that the total outdoor air (through windows, doors, cracks and SC all together) is no more than one air exchange rate when the VRF system is running. The simulation shows that the SC can reduce the annual ventilation energy by 77.8% and the VRF energy by 2.3%. Overall, the annual energy saving of the SC for the studied house model is 549.0 kWh or 9.0% of the total HVAC energy consumption.

Performance comparison between bypass cycle and injection cycle for sub-cooling methods in multi-split variable refrigerant flow (VRF) system in hot seasons

In this study, the performance of a multi-split VRF system using the bypass cycle and injection cycle is evaluated using the numerical simulation as a possible sub-cooling method to prevent flash gas generation in liquid pipelines. The simulation for the multi-split VRF system is developed by considering the applications of the bypass cycle and injection cycle, and is validated with experimental data. The bypass cycle and injection cycle in the multi-split VRF system yield improvements in their cooling capacities of the order of 3.22% and 13.43%, respectively, and energy efficient ratio (EER) of the order of 1.98% and 1.72%, respectively. The input power of the injection cycle is reduced by up to 4.45% when the performance of the multi-split VRF systems with bypass cycle and injection cycle is compared under the same cooling capacity conditions.

Application of the thermal comfort index as a control logic of VRF system and analysis of its performance by field measurement

Variable refrigerant flow (VRF) systems are known for their high energy performance and the installation of VRF systems in buildings is increasing. However, most studies have focused on improving the mechanical efficiency of VRF systems and there has been little research on energy efficient control strategies considering the thermal comfort of occupants. In this study, the application of the thermal comfort index as a control logic of VRF system was analyzed by field measurement. Indoor thermal environment and energy consumption characteristics according to control strategies such as set-point temperature and thermal comfort range in VRF system were analyzed. In this paper, the system configuration and the measurement results will be described.

Experimental investigation on oil-gas separator of air-conditioning systems

The oil-return system plays an important role in the variable refrigerant flow (VRF) systems because it ensures the reliable operation of the VRF systems. The oil-gas separator is the most essential component of the oil-return system, and the separation efficiency of the separator directly influences the performance of the VRF systems. Therefore, in this paper, a test rig was built to measure the oil discharge ratio of the compressor and the separation efficiency of the oil-gas separator. A sound velocity transducer was used to measure the oil mass concentration instantaneously, because the sound velocity was changed with the mass ratio of oil to refrigerant. The separation efficiency of the separator could be obtained by comparing the mass fraction of oil to refrigerant before and after the separator was connected to the system.

New method for measuring field performance of variable refrigerant flow systems based on compressor set energy conservation

In recent years, variable refrigerant flow (VRF) systems have gained popularity in commercial and residential buildings. It is extremely significant to obtain the real performance of VRF systems in order to improve design. However, the measurement of the field cooling or heating capacity of air-cooled VRF systems has not yet been thoroughly addressed. In this paper, a novel field measurement method for the capacity of VRF systems is proposed based on the energy conservation of a compressor set. The compressor set consists of the actual compressor, oil separator, and capillary with the corresponding pipelines. The validation results indicate that the compressor set energy conservation method yields 15% accuracy for cooling capacity and 13% accuracy for heating capacity. Furthermore, the novel measurement method was applied to a VRF system in an office building over four weeks during the heating season. The field test results demonstrate that the average part-load ratio of the VRF system was 0.33, which revealed that the sizing of the VRF system was relatively excessive.

An economic analysis of the integration between air-conditioning and solar photovoltaic systems

Current air conditioning systems have become consistently energy-efficient, but usually, these systems represent the largest electricity consumer in residential, commercial and industrial buildings. The variability in electricity costs also imposes uncertainties in the future operational costs of air conditioning installations. This study presents an economic analysis considering technical aspects related to the integrated use of solar photovoltaic, as a complementary energy source, and air conditioning systems connected to the utility grid. Two different systems, variable refrigerant flow and chillers, were analyzed in two cities in Brazil, Recife and São Paulo, with varying levels of irradiation and temperature. A mathematical model to evaluate the economic benefits of the integrated use of these systems was implemented considering conditionals and functional aspects of the locations, particularly in the Southern Hemisphere. A sensitivity analysis using Sobol’s method to determinate the most sensitive parameters affecting the economic results of the proposed system has been carried out. The results reveal high economic viability for all the systems studied and their most critical variables, namely energy price, energy price annual adjustment, photovoltaic cost and solar panel efficiency. Finally, the findings show that the implementation of air conditioning systems with solar photovoltaic energy could assure high internal rate of return for both cities, with average values around 28% for Recife and 22% for São Paulo, and the variable refrigerant flow system simulations resulted in better indexes when compared to chiller indexes.

Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system

Abstract The data-driven-based methods, which rely on history data, are the most common methods used in the fault diagnostics of building energy system because of their simplicity. However, a major problem with the application of data-driven methods is its interpretability due to the complicated algorithm theory and structure. This paper therefore proposes a methodology which is able to conduct both fault diagnosis and diagnostic knowledge discovery for building energy systems. A case study is implemented in an experimental variable refrigerant flow (VRF) system. The clustering of variable around latent variables (CLV) method is used for variable selection. Then, a classification-based-on-associations (CBA) classifier is set up for fault diagnosis based on the mined association rules. It achieves an overall diagnosis accuracy of 95.33%. In addition, the class association rules (CARs) of the classifier are visualized by grouped matrix-based method and graph-based method, respectively. Further, the CARs with high confidences and supports are interpreted by domain knowledge in the individual fault level. Results show that the diagnostic outcomes comply well with the expert knowledge. The underlying system operational characteristics at faulty conditions could be mined and understood. Moreover, the diagnostic outcomes provide a reasonable and reliable reference for further FDD researches.

Design and optimization of heating, cooling and lightening systems for a residential villa at Saman city, Iran

Purpose This paper, using energy softwares, designed of Iran and optimized a residential villa in Saman city located in Chaharmahal and Bakhtiari Province. Design/methodology/approach Having used the ideas of Climate Consultant software, the basic designing was conducted by Design Builder Software, and the cooling and heating loads and lighting tools and equipment were calculated. Then, the amount of consuming of heating, cooling and lighting load of the building was optimized through insulation of walls and ceiling, using green roof, double glazing UPVC windows, light intensity sensor and variable refrigerant flow (VRF) system. Findings Simulation results for the stated scenarios showed an annual reduction in energy consumption of 21.1, 7.9, 26.41, 27.3 and 72.3 per cent, respectively. Also, by combining all the five scenarios, an optimal state was achieved which, from the results, brought about an annual reduction of 86.9 per cent in the energy consumption. Originality/value The authors hope that the results of the current paper could be helpful for designers and engineers in reduction of energy consumption for designing a building in similar climatic conditions.

Performance analysis of multi-split variable refrigerant flow (VRF) system with vapor-injection in cold season

The performance of the variable refrigerant flow (VRF) system becomes poor at a very low ambient temperature because of the decrease in the suction mass flow rate. The vapor injection is applied to the heat pump system to enhance the performance of the systems in a cold season. In this study, the influence of the vapor injection under different intermediate pressures and outdoor ambient temperatures was investigated mathematically, and the results were validated with experimental data. In addition, the performance variation was observed with different indoor ambient temperatures in nine indoor units. The intermediate pressure affected the heating capacity, input power, coefficient of performance (COP), and isentropic efficiency of the compressor. The improvement of the COP with vapor injection was dominant at a low ambient temperature. The heating capacity at different indoor ambient temperatures was observed with different refrigerant mass flow rates in each indoor unit.

Abnormal energy identification of variable refrigerant flow air-conditioning systems based on data mining techniques

Identifying abnormal energy consumption in the variable refrigerant flow (VRF) system is important for its energy efficiency enhancement and energy saving. The energy performances of the VRF system are extremely distinct under various operation conditions. In order to evaluate the VRF’s energy performance, this study proposes a data-mining-based method to identify the abnormal energy consumption under different operation conditions. The local outlier factor (LOF) algorithm is used to distinguish transient data and three operation conditions are partitioned through clustering analysis. Besides, the correlation analysis is employed for key variables extraction. Then, the energy consumption is forecasted by support vector regression (SVR). The residuals of the measured energy values and the predicted values are applied in support vector data description (SVDD) algorithm to develop the responding D statistic as abnormal energy identification threshold. Finally, experiment data of various refrigerant charge level (RCL) are used to validate the proposed method. The methodology is sensitive to identifying abnormal energy consumption in VRFs at various RCLs. Nearly 100% of the abnormal energy consumption data can be accurately identified at some refrigerant charge levels. Operation condition partitioning can definitely enhance the identification efficiency.

An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems

This paper proposed a novel fault diagnosis strategy of the variable refrigerant flow (VRF) system based on expert rules for the first time. The VRF fault diagnosis rules (VFDR) are obtained through the expert knowledge and characteristics of the VRF system. The VFDR includes a total of 22 expert rules, 10 rules for the outdoor unit and other 12 rules for the indoor unit. The proposed VFDR can help maintenance personnel to identify and eliminate VRF faults in time. In addition to obtaining the coefficients of the sensor regression model, the VFDR does not require a training process and is computationally simple, making it easily embedded in the building’s automatic control system to achieve online fault diagnosis. The proposed fault diagnosis strategy is validated with nine faults of the VRF system under cooling mode. These faults contain temperature sensor faults, the system fault and indoor unit faults. The diagnosis correct rate (DCR) is used to evaluate the performance of the VFDR. Through expert rules fault diagnosis strategy, the DCRs of all faults exceed 70% and the overall DCR of all faults is 85.13%. The results show that faults of VRF system are well diagnosed by fault diagnosis strategy based on expert rules.

Influence of occupant behavior on the energy performance of variable refrigerant flow systems for office buildings: A case study

Currently, the controversies on the energy saving potential of Variable refrigerant flow (VRF) systems compared with other heating, ventilation, and air conditioning (HVAC) systems for office buildings still exist. In this study, a VRF system for an office building is investigated in field. The actual occupant behavior (OB) was presented statistically and imported to the energy model of VRF systems developed by the authors to compare the energy performance of VRF systems, with and without considering OB. The statistical results indicate the diversity in the on-duration periods and on-off times of each indoor unit (IU) is highly significant. According to the simulated results, the energy consumption of the VRF system under a continuous operation scheme is approximately 1.8 times that of the actual operation scheme owing to the OB. The total energy consumption of IUs with high air volume setting could be more than three times that of the actual operation scheme with a low air volume setting. In addition, the energy consumption intensity of the VRF system was presented by the parametric analysis on the different occupant thermostat setting temperature. Therefore, the OB contributes significantly to the energy performance of VRF systems, which should be considered for comparison with other HVAC systems.

Numerical analysis of control characteristics of variable refrigerant flow heat-pump systems focusing on the effect of expansion valve and indoor fan

In commercial and office buildings, it has recently become popular to install variable refrigerant flow (VRF) compression-type heat pump systems. In particular, VRF systems with multiple indoor units are being installed in large buildings because such systems have large adjustment capacity. On the other hand, because users of VRF systems can freely choose the number of indoor units to install and can turn indoor units on and off independently, operating conditions are not predictable and it is difficult to find adequate control parameters under wide changes in load. With this background, we are developing a new numerical simulation model based on the laws of physics. Because this model can easily add and delete system elements, we believe that it will be useful for VRF system analysis.In this study, we utilize a numerical simulation model to evaluate both steady and unsteady conditions in VRF systems. In this paper, we focus on the connection between manipulated variables (the expansion valve's open pulse and fan rotational speed) and controlled variables (supply air temperature and the degree of superheating at the evaporator outlet). Moreover, we compare the difference between the performance of a single indoor unit and that of multiple indoor units operating simultaneously.As a result, we can evaluate the dynamic characteristics of manipulated variables and the effect of changing the number of active indoor units, each of which has their own VRF characteristics.

Modelling of a Variable Refrigerant Flow System in EnergyPlus for Building Energy Simulation in an Open Building Information Modelling Environment

Variable refrigerant flow (VRF) systems are one possible tool to meet the objective that all new buildings must be nearly zero-energy buildings by 31 December 2020. Building Information Modelling (BIM) is a methodology that centralizes building construction project information in a digital model promoting collaboration between all its agents. The objectives of this work were to develop a more precise model of the VRF system than the one available in EnergyPlus version 8.9 (US Department of Energy) and to study the operation of this system in an office building under different climates by implementing the building energy simulation in an Open BIM workflow. The percentage deviation between the estimation of the VRF energy consumption with the standard and the new model was 6.91% and 1.59% for cooling and heating respectively in the case of Barcelona and 3.27% and 0.97% respectively in the case of Madrid. The energy performance class of the analysed building was A for each climatic zone. The primary energy consumption of the office building equipped with the VRF system was of 65.8 kWh/(m2·y) for the Mediterranean climate of Barcelona and 72.4 kWh/(m2·y) for the Continental climate of Madrid.