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Experimental validation of the simulation module of the water-cooled variable refrigerant flow system under cooling operation
Applied Energy 87 (2010) 1513–1521
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Experimental validation of the simulation module of the water-cooled variable refrigerant flow system under cooling operation Yue Ming Li a, Jing Yi Wu a,*, Sumio Shiochi b a b
Institute of Refrigeration and Cryogenics, Shanghai Jiao Tong University, Shanghai, China Daikin Industries, Ltd., 1304 Kanaoka-cho, Kita-ku, Sakai, Osaka 591-8511, Japan
a r t i c l e
i n f o
Article history: Received 25 June 2009 Received in revised form 8 September 2009 Accepted 19 September 2009
Keywords: Energy simulation Water-cooled variable refrigerant flow airconditioning system Cooling capacity Compressor power Validation
a b s t r a c t On the basis of EnergyPlus’s codes, the catalogue and performance parameters from some related companies, a special simulation module for variable refrigerant flow system with a water-cooled condenser (water-cooled VRF) was developed and embedded in the software of EnergyPlus, the building energy simulation program. To evaluate the energy performance of the system and the accuracy of the simulation module, the measurement of the water-cooled VRF is built in Dalian, China. After simulation and comparison, some conclusions can be drawn. The mean of the absolute value of the daily error in the 9 days is 11.3% for cooling capacity while the one for compressor power is 15.7%. At the same time, the accuracy of the power simulation strongly depends on the accuracy of the cooling capacity simulation. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction The variable refrigerant flow air-conditioning (VRF) system is composed of one outdoor unit and several indoor units, and can be regarded as a larger version of the split-type air-conditioning unit [1]. Different from the normal VRF system which is normally cooled by air (air-cooled VRF), a new developed system, the water-cooled variable refrigerant flow (water-cooled VRF) system, is cooled by water. The outdoor unit of the new system can be installed indoor and can be used in a building where no roof or external space is available for outdoor condensing units, and the system can also be fitted to an existing chilled water/cooling tower circuit. Moreover, there are many improved benefits in the new system which is accepted and gets popular in Europe and other lands [2,3]. To use energy more effectively, the prediction of annual energy consumption for different HVAC (heating, ventilation, and air conditioning) schemes are important to help designers or users to choose the best one. A number of papers address the research of modeling and simulating the energy features of HVAC systems using commercial programs or mathematical methods [4–9]. In most of them the system is simulated with the calculation of the thermal properties of building simultaneously. But for the watercooled VRF system, there is rarely research on the annual energyconsumption characteristics from the perspective of dynamic * Corresponding author. Tel.: +86 21 34206776; fax: +86 21 34206309. E-mail address: [email protected] (J.Y. Wu). 0306-2619/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2009.09.018
building energy simulation. There are also short of both test data sets and experimentally validated simulation design tools of the system. EnergyPlus is a building energy simulation program for modeling building heating, cooling, lighting, ventilating, and other energy flows, and it is an improved one based on the most popular features and capabilities of BLAST and DOE-2 [10,11]. The application, accuracy and parametric analysis of the program have been reported in the papers [12–14]. One of the advantages for the user and other developers is that the program is designed for ease of development. The concept is that many people can contribute to EnergyPlus and the program structure has been designed to make this possible [17]. On the basis of EnergyPlus’s codes, and using the manufacturer’s performance parameters and data, a simulation module of air-cooled VRF system was developed and validated experimentally to evaluate the energy features [15,16]. Considering a similar simulation method, a simulation module of water-cooled VRF system was also developed and embedded in the software [19], and the model can be used as a tool for the designer or the user to calculate or analyze the dynamic energy characteristics and the energy flows between the system and the building load. In Ref. [19], the modeling process and some analysis of the simulation results about the new model for the new system were explained and finished. Furthermore, in this paper the experiment validation work about the new simulation model is investigated. This work is very important because it is an effective technique to exam the
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Nomenclature RE relative error jREj absolute value of relative error x variable of simulated value or measured data cooling capacity, K Wh Qc compressor power, K Wh Pc R correlation coefficient C v ðRMSEÞ coefficient of variation of the root-mean-squared error
effectiveness and correctness of the implemented algorithms in the new simulation model. In the first part of this paper there is a simple introduction for the system and the main information about the simulation model. Subsequently, the descriptions about the building and the experimental setup are presented, which include the detailed information of measuring methods and devices, the construction and configuration of the architecture, the number of the internal load as well as the conditions of the outdoor environment. In the third part hourly and daily comparison results between experiment and simulation are analyzed. At last, some statistical and correlation analysis is also finished in this part. 2. Simulation model for water-cooled VRF The condenser of water-cooled VRF system is cooled by water, and the schematic diagram for the whole loop of water-cooled VRF system can be shown in Fig. 1 [19]. There are two main loops in the water-cooled VRF system. Water loop links with the cooling tower and outdoor unit, and the condensing heat can be rejected into the outside environment. Refrigerant loop links with the outdoor unit and indoor unit, and indoor unit can make a heat exchange directly between refrigerant and indoor air. In addition, one cooling tower can be linked with several outdoor units and supply cooling water to them. (Fig. 1 only shows one outdoor unit in the water loop.) For the power simulation, in Energyplus the model is setup according to the theory of performance curve-oriented way [17]. So the new model adopts the similar method. The basic mathematical model of the water-cooled VRF system refers to the simulation method of the air-cooled VRF system and the existing chiller model in Energyplus. The main equations about the mathematical model are shown in Ref. [19]. The total cooling capacity of all the indoor units connected to one outdoor unit is the summation of all the DX (direct expansion) coils’ cooling capacity when using DX coil model as the based one. On the basis of the real total cooling capacity, the real power of the compressor can be calculated by the mathematical equations in Ref. [19]. For the simulation of the water loop in the water-cooled system, EnergyPlus uses a loop based HVAC system formulation. The overall structure of the loop is defined with branch and connector objects. The detail is filled with components and their inlet and outlet nodes [17]. Considering this method, the outdoor unit of the water-cooled VRF system can be linked with the cooling tower and pump’s models already existing in EnergyPlus [19]. Based on the above simulation methods, with the EnergyPlus coding style the coding work for the simulation module is established in the EnergyPlus source code. The interface about the input file is also modified and added a group for the input of the new model which mainly includes the needed information and parameters for the energy simulation of the system. Besides, the new module is also embedded in the top manage module of the calling tree about the HVAC system’s simulation, then it can be called and
r n
standard deviation sample size
Subscripts i number index of sample s simulated data m measured data
calculated. The detailed information about the codes programming is shown in Ref. [19].
3. Building and experimental system 3.1. Simple introduction of building The multifunctional building is located in Dalian, China. The building area is 100 thousand square meters and the building is used for apartment, office and commercial center. The construction of the building is a rebuilding one. Two main towers use watercooled VRF systems and the annex is cooled by central air-conditioning systems. One of the two tower buildings is apartment building, and the other is office building.
3.2. Experimental setup The actual calculating area is about half of the 22nd floor. The target system is mainly composed of one outdoor unit and 10 indoor units. The combination ratio (the ratio of the total rated capacity of indoor units to the rated capacity of outdoor unit) is about 1.1 under cooling condition. The setup is located in the north side of the 22nd floor, with ten assumed zones in charge. Normally, the system operating schedule is from 9:00 to 17:00 on weekdays. Fig. 2 shows the information about the testing system about the floor plan of the building and the experimental setup of the watercooled VRF system. The orange broken line gives the rough zone measured and controlled in the experiment. Sensors installed in indoor units and outdoor unit of the system can collect the detailed operation parameters such as the temperature and pressure of refrigerant flowing at each important point, and send them to a checker for data recording. Then the checker data can be gotten and managed by a computer. Therefore, cooling capacity evaluation for the system can be achieved by using the monitoring temperatures, pressures of refrigerant, and the compressor performance curve. Electricity power can be obtained by sensors to measure electric current of the input to compressor (outdoor unit). The suction temperature of each indoor unit is considered to be approximate to the indoor temperature. The accuracy of these sensors is within ±0.5% and the shortest time step of data recording can be 1 min. The deviation of the cooling capacity and electricity power are confirmed to be within ±10%. There are two inlets and two outlets of water piping for one outdoor unit because the outdoor unit is made of two condenser units. Four thermometers with the accuracy of ±0.3 °C are installed on the water pipes to get water temperature data. Pressure difference of the water flow rate in each pipe under a standard condition is adjusted and measured. According to the relation between the pressure difference and the flow rate (see Ref. [18]) the water flow rate and the flow ratio of the two water pipes under the standard condition can be calculated. By using the law of conservation of energy, the hourly real water flow rate can be calculated by the
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Pump Cooling tower
Water loop
Condenser
Refrigerant loop Expansion valve n
Expansion valve k
Expansion valve 1
Expansion valve 1
Compressor
Fan 1
Evaporator 1 Fan 2 Evaporator 2
Fan k Evaporator k
Fan n Evaporator n Fig. 1. Schematic diagram of the whole loop of water-cooled VRF system.
measured data of cooling capacity, compressor power and water temperature. 4. Simulation models and validation 4.1. Simulation parameters of building The measured system is not a mimic system and it is in a real office building. The total area of the air conditioning zone is 450 m2 approximately. The building characteristics are acquired from the building survey and the as-built blueprints, specifications and documentation. The exterior wall consists of 8 mm ceramic tile, 20 mm mortar, 150 mm concrete, 25 mm foam polystyrene, closed air and 12 mm plaster board. The interior walls are mainly of a 120 mm wide aerated concrete block with both sides covered by 20 mm mortar. Fig. 3 gives the structure of the windows. The window wall ratio (WWR) of the external wall is about 38%. The single-pane glazing has a transmittance of 21.7% and reflectance of 4.4%. The neighboring space on the south side has no sensors installed. Temperatures in these zones are regarded as the ones in air-conditioning zones with a set point of
26 °C. According to Ref. [16], to reduce the difference of the simulated temperature and test data, the measured indoor temperature (the suction temperature of the indoor unit) on an hourly basis is input to the software, instead of the constant set point. Table 1 shows the parameters about the internal load in a normal condition. The actual quantity of internal load changes mostly within a range of 50–100% of the values shown in Table 1. The conditions of building before the test affect the test results. One of the important factors is the number of the internal load. In this measurement, before the test the working condition of all the internal equipment and people was similar as that in the period of the test. Besides, all the HVAC systems worked on the daytime. That is to say, before the test, the building internal load and the system were in a stable operation condition. The free hourly weather data of the nearby airport in Dalian can be obtained from the website. However, the detailed value of radiation is not provided, and it is set according to two kinds of information and data. One is the actual rough weather condition information, and the other is Chinese Typical Year Weather data in Dalian available in Energyplus.
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Fig. 2. Schematic diagram of the test system (unit: mm).
4.2. Weather condition of test days
4.3. Results and discussion
Measured data of nine test days in August and September 2008 are supplied and can be used to do the validation. The city of Dalian is in the north of China, and the weather in summer is a little cooler than other south cities. But for a large office building, the cooling load will be large because of the large internal load. Table 2 gives the weather condition of the test days. In the 9 days, the maximum dry-bulb temperature is 29 °C, and the average one is about 22.7 °C. The daily average relative humidity ratio is from 75% to 96%. On the day before test, the average temperature is 24 °C, and the average humidity is 90%.
4.3.1. Hourly profile and relative error As a building energy simulation program, Energyplus calculates so many parameters that all the simulation errors will not be very small. There are two important reasons to cause the simulation error of Energyplus, one is the modeling and calculating method of the building energy simulation program itself, and the other is the uncertain and uncontrolled information on all the input parameters of the input file. All of these factors such as the complicated thermal bridge of the wall, the random activity of people, the actual data of sun radiation, and so on will cause some difference
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Fig. 3. Structure of window (unit: mm).
Table 1 Parameters of the internal load. Type of internal load
Parameters
Lighting People Equipment
11 W/m2 4 m2/person 20 W/m2
Table 2 Weather parameters of test days. Sequence number of test days
Average dry-bulb temperature (°C)
Maximum drybulb temperature (°C)
Average relative humidity ratio (%)
1 2 3 4 5 6 7 8 9
22 22 20 25 23 22 22 24 24
23 25 22 29 27 25 28 26 27
95 88 94 90 87 85 75 96 80
40
100% Simulated data Measured data
35
80%
30 60%
20 40%
15
|RE|
Qc (Kwh)
25
10 20% 5 0 10:00-11:00
12:00-13:00
14:00-15:00
16:00-17:00
Time Fig. 4. Comparison between measured and simulated values of cooling capacity on last day.
between the simulation and experiment. For example, simplified method used in the software to calculate the thermal bridge may cause 2–44% of the simulation error for the heat transfer through different kinds of wall (Ref. [22]), then this will cause the simulation error of cooling load. So in the validation studies of the
building energy simulation program, the simulation error did not have a very small range. Define the relative error as follows:
RE ¼
x s xm 100% xs
xs xm 100% jREj ¼ xs
ð1Þ
ð2Þ
The absolute value of the relative error can remove the influence of the sign, and it is one of the main parameters used to do the comparison analysis. Considering the above uncertain factors, most of the hourly relative error is in the range of 25% for the total simulation period. Besides the relative error, in most hours the hourly profile of simulation is similar to the one of measurement. Figs. 4 and 5 give the comparison of hourly data on the last test day, and the similar profile between measurement and simulation can be showed. The mean of the absolute value of the hourly relative error of cooling capacity on this day is 22.4%. The mean of the absolute value of the hourly compressor power error is 30.2% if the last two hours are excluded, and is 33.9% if included. Simulation results of other days show that the compressor power differences of most other days will be less than that of this day. Fig. 6 shows the comparison of COP (coefficient of performance). In most hours, the simulated COP is near the measured one except for the last two hours. On the actual condition, near the end of working hours on each day, the performance of the whole system had some fluctuation, which can be attributed to the different stop time of each indoor unit and other bad influence due to uncontrolled factors such as the unstable state of the internal load and system close-down procedure. Consequently, COP at these hours for the actual system will have a little bigger difference from the simulated one than that at other hours, which also results in the big simulation error of compressor power at the end working time of the day. If the last two hours are excluded, the mean of the absolute value of the hourly relative error is 4.8%, and if included the one becomes to be 8.3%. For other days, in some hours such as the beginning time or noontime, the simulation error of COP may be large because the performance curve from manufacturer is typically not able to fully reflect the transient feature in the water-cooled VRF startup procedure or the unstable operation of the system at the break time. 4.3.2. Comparison results of total hourly data Fig. 7 shows that about 81% of all the measured data fall within ±25% of the simulated data for cooling capacity. The mean of the absolute value of the hourly relative error for the total simulation period is 15.6%. Fig. 8 shows that about 64% or 83% of all the measured data for compressor power fall within ±30% or ±35% of
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8
100%
+35% +30%
80%
Pc (kwh)
6 60%
|RE|
5 40%
4 20%
3 2
Measured compressor power (w)
10000
Simulated data Measured data
7
11000
10:00-11:00 12:00-13:00 14:00-15:00 16:00-17:00
9000 8000
y=x
7000 6000
-30% -35%
5000 4000 3000 2000 1000
Time
1000
2000
3000
4000
5000
6000
7000
8000
9000
Simulated compressor power (W)
Fig. 5. Comparison between measured and simulated values of compressor power on last day.
Fig. 8. Hourly comparison results of compressor power.
100%
7 Simulated data Measured data
6
9
+20%
80%
y=x 8
5
40%
3 20%
2
7
Measured COP
4
|RE|
COP
60%
-20%
6
5
1 10:00-11:00
12:00-13:00 14:00-15:00
16:00-17:00
4
Time Fig. 6. Comparison between measured and simulated values of COP on last day.
3 3
4
5
6
7
8
9
Simulated COP 45000
Fig. 9. Hourly comparison results of COP.
25%
Measured cooling capacity (w)
40000
y=x 35000 30000 25000
-25%
20000 15000 10000 5000 5000
10000
15000
20000
25000
30000
35000
40000
Simulated cooling capacity (W) Fig. 7. Hourly comparison results of cooling capacity.
the simulated data respectively. The average hourly relative error for the total simulation period is 23.0%. Fig. 9 shows that for COP about 79% of all the measured data fall within ±20% of the simulated data. The mean of the absolute value of the hourly relative error for the total simulation period is 12.3%. In most hours, the simulated COP is higher than the measured one.
For a building energy simulation program, not all the hourly simulation errors are very small which is mainly caused by some uncertain and uncontrolled changes of the real internal loads and the outdoor weather conditions between hours. Much past research and simulation reports show some similar results. In Ref. [20], the simulation result of Energyplus also shows that the hourly difference of power between simulation and measurement can achieve 50% or even more which is mainly caused by the inaccuracy of hourly weather data. In Ref. [21], in some validation example for DOE, the difference of hourly total energy consumption between simulation and measurement can achieve more than 30%. So it is costly and hard to do a very accurate hourly validation for a complicated energy simulation model. When the period of comparison is longer, the average influence of all the uncertain factors will always become reduced. So the computed results are mainly in better agreement for longer time periods (Ref. [21]). In Ref. [20], there is a better agreement between simulated and measured data on a monthly basis. In Ref. [21], the results also show that the simulation error of monthly or annual is smaller than the one of hourly data. Consequently, the period of validation comparison is normally longer than an hour (use daily data or monthly data).
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320
100%
7
80%
6
Measured data Simulated data
Measured data Simulated data
280 240
5
160 40%
120
COP
60%
|RE|
Qc (Kwh)
200
4 3
80 20%
2
40 0 0
2
4
6
8
1
10
Day
0 0
Fig. 10. Measured and simulated daily cooling capacity.
1
2
3
4
5
6
7
8
9
10
Day Fig. 12. Measured and simulated daily COP.
50
100% 80%
40
60%
30
Measured data Simulated data
20
40%
|RE|
Pc (kwh)
Table 3 Correlation of simulated and measured data. Items
Cooling capacity
Compressor power
Correlation coefficient (R) statistic of t-test (t) Sample size (n)
0.861 4.48
0.909 5.78 9
4.3.4. Statistical and correlation analysis 20%
10
(1) Statistical and correlation analysis between simulated and measured values.
0 0
2
4
6
8
10
Day Fig. 11. Measured and simulated daily compressor power.
4.3.3. Daily profile and relative error In the research of building energy simulation, one day is normally the basic unit for the annual or monthly simulation and analysis. Figs. 10 and 11 present the comparison between measurements and simulated results of cooling capacity and compressor power consumption respectively. The greatest absolute value of the daily relative error of compressor power achieves 33.8%. The mean of the absolute value of the daily relative error of the 9 days is 15.7%. For the cooling capacity, the maximum absolute value of the daily relative error achieves 26.4%. The mean of the 9 days is 11.3%, a little lower than that of the compressor power. Although the error value is not the same in all the days, both the simulated cooling capacity and the simulated compressor power have similar evolvement profiles to the measured data. Fig. 12 gives the comparison of daily COP. The mean of the absolute value of the daily relative error is 7.60% while the maximum is 18.1%. The absolute value of the relative error in 6 days is less than 10%, and in 4 days is less than 5%. Only in one day it is bigger than 15%. Simulated COP is higher than measured one, that is, the performance of simulation is a little better than the real one. Under a real condition there are many factors that can cause bad influences on the system, and it is difficult to take into account all these factors in the simulation. Normally, the simulation model can be more correct in the stable and normal working conditions.
The statistical results can give the prediction of a sample population. To find the relations between measured cooling capacity (compressor power) and simulated cooling capacity (compressor power), Table 3 gives the correlation analysis of the daily data. The correlation coefficient can give the consistency and dependency between the simulation and experiment. The calculation equation of correlation coefficient is as follow:
Pn i¼1 ðxsi xs Þðxmi xm Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P Pn n 2 2 i¼1 ðxsi xs Þ i¼1 ðxmi xm Þ
ð3Þ
As presented in Table 3, both of the correlation coefficients are more than 0.85, which indicates high correlation between the simulated and measured values. The correlation hypotheses are verified through a t-test in a confidence level of 95% (t > t a=2 ðn 2Þ ¼ 2:36). The hypotheses of significant correlation are accepted. Cv(RMSE), a value of normalized measure of variability between simulated and measured data, is defined as the following equation:
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Pn 2 i¼1
C v ðRMSEÞ ¼
ðxs xm Þ n
xs
ð4Þ
The values of Cv(RMSE) in Table 4 for cooling capacity and COP are less than 15% in the simulation period of 9 test days, which reflects a small variability between simulation and measurement. The value of Cv(RMSE) for compressor power is a little bigger. (2) Statistical and correlation analysis of relative error Table 5 gives the standard deviation of the absolute value of the daily relative error for cooling capacity, compressor power and
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Table 4 Values of Cv(RMSE).
5. Conclusions
Items
Cv(RMSE) (%)
Cooling capacity Compressor power COP
14.5 20.4 9.9
Table 5 Standard deviation of the absolute value of relative error. Item
r (%)
Cooling capacity Compressor power COP
8.4 12.1 5.5
Table 6 Relation between the daily relative error of cooling capacity and that of compressor power. Item
Value
Sample size (n) Correlation coefficient (R) Statistic of t-test (t)
9 0.897 5.36
COP. All the standard deviations of the absolute value of the relative error are less than 15%, which can verify the small deviation from the arithmetic mean of the absolute values of the relative error.
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 Pn 2 1 i¼1 jREji n i¼1 jREji r¼ n1
ð5Þ
Same as the air-cooled model [16], there are two kinds of sequential factors affecting the last validation results of the water-cooled VRF simulation model. The first one is the accuracy of the EnergyPlus simulation model itself which mainly includes the correctness of all the input parameters in the input file. The second is the correctness of the modeling strategy and algorithm of the new module for the water-cooled VRF system. In this simulation, the cooling capacity is calculated and its accuracy is mainly decided by the original engine of EnergyPlus (correctness of input file). At the same time, the simulation accuracy of cooling capacity can affect the one of compressor power. Consequently, it is instructive to analyze the relation between the simulation error of cooling capacity and the one of compressor power. Table 6 gives the relations between the daily relative error of cooling capacity and that of compressor power. The similar results can also be seen in all hourly data. The hypotheses of significant linear correlation between the daily relative error of cooling capacity and that of compressor power are checked up and accepted using t-test in a confidence level of 95% t > t a=2 ðn 2Þ ¼ 2:36 . The significant linear correlation of the two variables can verify the strong dependency between simulation accuracy of compressor power and that of cooling capacity. Because the value of correlation coefficient is bigger than 0 (0 < R < 1), which means positive correlation, if the daily relative error of cooling capacity increases/ decreases, the one of compressor power will increase/decrease too. In other words, the simulation accuracy of the compressor power (mainly decided by the new simulation module) can track the accuracy of cooling capacity (mainly decided by the input file of EnergyPlus).
In this study, the water-cooled VRF simulation module is developed in the building energy analysis program, EnergyPlus, and validated by experimental data. The computer simulation of the water-cooled VRF air-conditioning system agrees with the monitored performance within generally acceptable accuracy. The main findings obtained from this work are presented as follows. (1) About 81% of all the measured data fall within ±25% of the simulated data for cooling capacity. The mean of the absolute value of the hourly relative error for the total simulation period is 15.6%. About 64% or 83% of all the measured data for compressor power fall within ±30% or ±35% of the simulated data respectively. The average hourly relative error for the total simulation period is 23.0%. (2) The greatest absolute value of the daily relative error of compressor power achieves 33.8%. The mean of the absolute value of the daily relative error of the 9 days is 15.7%. For the cooling capacity, the maximum absolute value of the daily relative error achieves 26.4%, and the mean of that of the 9 days is 11.3%. (3) The correlation coefficients between simulated cooling capacity (compressor power) and measured cooling capacity (compressor power) are more than 0.85, which reflects that high correlation exists between the simulated and measured data. (4) The simulation accuracy of the compressor power can track the accuracy of cooling capacity. In a word, many factors, which include the ideal assumption made in the new simulation model, the unstable and bad working conditions of experiment in some time, the measurement difference of all the devices and so on, can induce errors in the simulation. At the same time, a good description of the architecture in the input file is also an important factor leading to correct simulation results. That is to say, when the EnergyPlus model (input file) can reflect the real building load, the water-cooled VRF system module is able to reflect the actual compressor power with confidence.
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Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Experimental validation of the simulation module of the water-cooled variable refrigerant flow system under cooling operation Yue Ming Li a, Jing Yi Wu a,*, Sumio Shiochi b a b
Institute of Refrigeration and Cryogenics, Shanghai Jiao Tong University, Shanghai, China Daikin Industries, Ltd., 1304 Kanaoka-cho, Kita-ku, Sakai, Osaka 591-8511, Japan
a r t i c l e
i n f o
Article history: Received 25 June 2009 Received in revised form 8 September 2009 Accepted 19 September 2009
Keywords: Energy simulation Water-cooled variable refrigerant flow airconditioning system Cooling capacity Compressor power Validation
a b s t r a c t On the basis of EnergyPlus’s codes, the catalogue and performance parameters from some related companies, a special simulation module for variable refrigerant flow system with a water-cooled condenser (water-cooled VRF) was developed and embedded in the software of EnergyPlus, the building energy simulation program. To evaluate the energy performance of the system and the accuracy of the simulation module, the measurement of the water-cooled VRF is built in Dalian, China. After simulation and comparison, some conclusions can be drawn. The mean of the absolute value of the daily error in the 9 days is 11.3% for cooling capacity while the one for compressor power is 15.7%. At the same time, the accuracy of the power simulation strongly depends on the accuracy of the cooling capacity simulation. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction The variable refrigerant flow air-conditioning (VRF) system is composed of one outdoor unit and several indoor units, and can be regarded as a larger version of the split-type air-conditioning unit [1]. Different from the normal VRF system which is normally cooled by air (air-cooled VRF), a new developed system, the water-cooled variable refrigerant flow (water-cooled VRF) system, is cooled by water. The outdoor unit of the new system can be installed indoor and can be used in a building where no roof or external space is available for outdoor condensing units, and the system can also be fitted to an existing chilled water/cooling tower circuit. Moreover, there are many improved benefits in the new system which is accepted and gets popular in Europe and other lands [2,3]. To use energy more effectively, the prediction of annual energy consumption for different HVAC (heating, ventilation, and air conditioning) schemes are important to help designers or users to choose the best one. A number of papers address the research of modeling and simulating the energy features of HVAC systems using commercial programs or mathematical methods [4–9]. In most of them the system is simulated with the calculation of the thermal properties of building simultaneously. But for the watercooled VRF system, there is rarely research on the annual energyconsumption characteristics from the perspective of dynamic * Corresponding author. Tel.: +86 21 34206776; fax: +86 21 34206309. E-mail address: [email protected] (J.Y. Wu). 0306-2619/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2009.09.018
building energy simulation. There are also short of both test data sets and experimentally validated simulation design tools of the system. EnergyPlus is a building energy simulation program for modeling building heating, cooling, lighting, ventilating, and other energy flows, and it is an improved one based on the most popular features and capabilities of BLAST and DOE-2 [10,11]. The application, accuracy and parametric analysis of the program have been reported in the papers [12–14]. One of the advantages for the user and other developers is that the program is designed for ease of development. The concept is that many people can contribute to EnergyPlus and the program structure has been designed to make this possible [17]. On the basis of EnergyPlus’s codes, and using the manufacturer’s performance parameters and data, a simulation module of air-cooled VRF system was developed and validated experimentally to evaluate the energy features [15,16]. Considering a similar simulation method, a simulation module of water-cooled VRF system was also developed and embedded in the software [19], and the model can be used as a tool for the designer or the user to calculate or analyze the dynamic energy characteristics and the energy flows between the system and the building load. In Ref. [19], the modeling process and some analysis of the simulation results about the new model for the new system were explained and finished. Furthermore, in this paper the experiment validation work about the new simulation model is investigated. This work is very important because it is an effective technique to exam the
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Nomenclature RE relative error jREj absolute value of relative error x variable of simulated value or measured data cooling capacity, K Wh Qc compressor power, K Wh Pc R correlation coefficient C v ðRMSEÞ coefficient of variation of the root-mean-squared error
effectiveness and correctness of the implemented algorithms in the new simulation model. In the first part of this paper there is a simple introduction for the system and the main information about the simulation model. Subsequently, the descriptions about the building and the experimental setup are presented, which include the detailed information of measuring methods and devices, the construction and configuration of the architecture, the number of the internal load as well as the conditions of the outdoor environment. In the third part hourly and daily comparison results between experiment and simulation are analyzed. At last, some statistical and correlation analysis is also finished in this part. 2. Simulation model for water-cooled VRF The condenser of water-cooled VRF system is cooled by water, and the schematic diagram for the whole loop of water-cooled VRF system can be shown in Fig. 1 [19]. There are two main loops in the water-cooled VRF system. Water loop links with the cooling tower and outdoor unit, and the condensing heat can be rejected into the outside environment. Refrigerant loop links with the outdoor unit and indoor unit, and indoor unit can make a heat exchange directly between refrigerant and indoor air. In addition, one cooling tower can be linked with several outdoor units and supply cooling water to them. (Fig. 1 only shows one outdoor unit in the water loop.) For the power simulation, in Energyplus the model is setup according to the theory of performance curve-oriented way [17]. So the new model adopts the similar method. The basic mathematical model of the water-cooled VRF system refers to the simulation method of the air-cooled VRF system and the existing chiller model in Energyplus. The main equations about the mathematical model are shown in Ref. [19]. The total cooling capacity of all the indoor units connected to one outdoor unit is the summation of all the DX (direct expansion) coils’ cooling capacity when using DX coil model as the based one. On the basis of the real total cooling capacity, the real power of the compressor can be calculated by the mathematical equations in Ref. [19]. For the simulation of the water loop in the water-cooled system, EnergyPlus uses a loop based HVAC system formulation. The overall structure of the loop is defined with branch and connector objects. The detail is filled with components and their inlet and outlet nodes [17]. Considering this method, the outdoor unit of the water-cooled VRF system can be linked with the cooling tower and pump’s models already existing in EnergyPlus [19]. Based on the above simulation methods, with the EnergyPlus coding style the coding work for the simulation module is established in the EnergyPlus source code. The interface about the input file is also modified and added a group for the input of the new model which mainly includes the needed information and parameters for the energy simulation of the system. Besides, the new module is also embedded in the top manage module of the calling tree about the HVAC system’s simulation, then it can be called and
r n
standard deviation sample size
Subscripts i number index of sample s simulated data m measured data
calculated. The detailed information about the codes programming is shown in Ref. [19].
3. Building and experimental system 3.1. Simple introduction of building The multifunctional building is located in Dalian, China. The building area is 100 thousand square meters and the building is used for apartment, office and commercial center. The construction of the building is a rebuilding one. Two main towers use watercooled VRF systems and the annex is cooled by central air-conditioning systems. One of the two tower buildings is apartment building, and the other is office building.
3.2. Experimental setup The actual calculating area is about half of the 22nd floor. The target system is mainly composed of one outdoor unit and 10 indoor units. The combination ratio (the ratio of the total rated capacity of indoor units to the rated capacity of outdoor unit) is about 1.1 under cooling condition. The setup is located in the north side of the 22nd floor, with ten assumed zones in charge. Normally, the system operating schedule is from 9:00 to 17:00 on weekdays. Fig. 2 shows the information about the testing system about the floor plan of the building and the experimental setup of the watercooled VRF system. The orange broken line gives the rough zone measured and controlled in the experiment. Sensors installed in indoor units and outdoor unit of the system can collect the detailed operation parameters such as the temperature and pressure of refrigerant flowing at each important point, and send them to a checker for data recording. Then the checker data can be gotten and managed by a computer. Therefore, cooling capacity evaluation for the system can be achieved by using the monitoring temperatures, pressures of refrigerant, and the compressor performance curve. Electricity power can be obtained by sensors to measure electric current of the input to compressor (outdoor unit). The suction temperature of each indoor unit is considered to be approximate to the indoor temperature. The accuracy of these sensors is within ±0.5% and the shortest time step of data recording can be 1 min. The deviation of the cooling capacity and electricity power are confirmed to be within ±10%. There are two inlets and two outlets of water piping for one outdoor unit because the outdoor unit is made of two condenser units. Four thermometers with the accuracy of ±0.3 °C are installed on the water pipes to get water temperature data. Pressure difference of the water flow rate in each pipe under a standard condition is adjusted and measured. According to the relation between the pressure difference and the flow rate (see Ref. [18]) the water flow rate and the flow ratio of the two water pipes under the standard condition can be calculated. By using the law of conservation of energy, the hourly real water flow rate can be calculated by the
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Pump Cooling tower
Water loop
Condenser
Refrigerant loop Expansion valve n
Expansion valve k
Expansion valve 1
Expansion valve 1
Compressor
Fan 1
Evaporator 1 Fan 2 Evaporator 2
Fan k Evaporator k
Fan n Evaporator n Fig. 1. Schematic diagram of the whole loop of water-cooled VRF system.
measured data of cooling capacity, compressor power and water temperature. 4. Simulation models and validation 4.1. Simulation parameters of building The measured system is not a mimic system and it is in a real office building. The total area of the air conditioning zone is 450 m2 approximately. The building characteristics are acquired from the building survey and the as-built blueprints, specifications and documentation. The exterior wall consists of 8 mm ceramic tile, 20 mm mortar, 150 mm concrete, 25 mm foam polystyrene, closed air and 12 mm plaster board. The interior walls are mainly of a 120 mm wide aerated concrete block with both sides covered by 20 mm mortar. Fig. 3 gives the structure of the windows. The window wall ratio (WWR) of the external wall is about 38%. The single-pane glazing has a transmittance of 21.7% and reflectance of 4.4%. The neighboring space on the south side has no sensors installed. Temperatures in these zones are regarded as the ones in air-conditioning zones with a set point of
26 °C. According to Ref. [16], to reduce the difference of the simulated temperature and test data, the measured indoor temperature (the suction temperature of the indoor unit) on an hourly basis is input to the software, instead of the constant set point. Table 1 shows the parameters about the internal load in a normal condition. The actual quantity of internal load changes mostly within a range of 50–100% of the values shown in Table 1. The conditions of building before the test affect the test results. One of the important factors is the number of the internal load. In this measurement, before the test the working condition of all the internal equipment and people was similar as that in the period of the test. Besides, all the HVAC systems worked on the daytime. That is to say, before the test, the building internal load and the system were in a stable operation condition. The free hourly weather data of the nearby airport in Dalian can be obtained from the website. However, the detailed value of radiation is not provided, and it is set according to two kinds of information and data. One is the actual rough weather condition information, and the other is Chinese Typical Year Weather data in Dalian available in Energyplus.
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Fig. 2. Schematic diagram of the test system (unit: mm).
4.2. Weather condition of test days
4.3. Results and discussion
Measured data of nine test days in August and September 2008 are supplied and can be used to do the validation. The city of Dalian is in the north of China, and the weather in summer is a little cooler than other south cities. But for a large office building, the cooling load will be large because of the large internal load. Table 2 gives the weather condition of the test days. In the 9 days, the maximum dry-bulb temperature is 29 °C, and the average one is about 22.7 °C. The daily average relative humidity ratio is from 75% to 96%. On the day before test, the average temperature is 24 °C, and the average humidity is 90%.
4.3.1. Hourly profile and relative error As a building energy simulation program, Energyplus calculates so many parameters that all the simulation errors will not be very small. There are two important reasons to cause the simulation error of Energyplus, one is the modeling and calculating method of the building energy simulation program itself, and the other is the uncertain and uncontrolled information on all the input parameters of the input file. All of these factors such as the complicated thermal bridge of the wall, the random activity of people, the actual data of sun radiation, and so on will cause some difference
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Fig. 3. Structure of window (unit: mm).
Table 1 Parameters of the internal load. Type of internal load
Parameters
Lighting People Equipment
11 W/m2 4 m2/person 20 W/m2
Table 2 Weather parameters of test days. Sequence number of test days
Average dry-bulb temperature (°C)
Maximum drybulb temperature (°C)
Average relative humidity ratio (%)
1 2 3 4 5 6 7 8 9
22 22 20 25 23 22 22 24 24
23 25 22 29 27 25 28 26 27
95 88 94 90 87 85 75 96 80
40
100% Simulated data Measured data
35
80%
30 60%
20 40%
15
|RE|
Qc (Kwh)
25
10 20% 5 0 10:00-11:00
12:00-13:00
14:00-15:00
16:00-17:00
Time Fig. 4. Comparison between measured and simulated values of cooling capacity on last day.
between the simulation and experiment. For example, simplified method used in the software to calculate the thermal bridge may cause 2–44% of the simulation error for the heat transfer through different kinds of wall (Ref. [22]), then this will cause the simulation error of cooling load. So in the validation studies of the
building energy simulation program, the simulation error did not have a very small range. Define the relative error as follows:
RE ¼
x s xm 100% xs
xs xm 100% jREj ¼ xs
ð1Þ
ð2Þ
The absolute value of the relative error can remove the influence of the sign, and it is one of the main parameters used to do the comparison analysis. Considering the above uncertain factors, most of the hourly relative error is in the range of 25% for the total simulation period. Besides the relative error, in most hours the hourly profile of simulation is similar to the one of measurement. Figs. 4 and 5 give the comparison of hourly data on the last test day, and the similar profile between measurement and simulation can be showed. The mean of the absolute value of the hourly relative error of cooling capacity on this day is 22.4%. The mean of the absolute value of the hourly compressor power error is 30.2% if the last two hours are excluded, and is 33.9% if included. Simulation results of other days show that the compressor power differences of most other days will be less than that of this day. Fig. 6 shows the comparison of COP (coefficient of performance). In most hours, the simulated COP is near the measured one except for the last two hours. On the actual condition, near the end of working hours on each day, the performance of the whole system had some fluctuation, which can be attributed to the different stop time of each indoor unit and other bad influence due to uncontrolled factors such as the unstable state of the internal load and system close-down procedure. Consequently, COP at these hours for the actual system will have a little bigger difference from the simulated one than that at other hours, which also results in the big simulation error of compressor power at the end working time of the day. If the last two hours are excluded, the mean of the absolute value of the hourly relative error is 4.8%, and if included the one becomes to be 8.3%. For other days, in some hours such as the beginning time or noontime, the simulation error of COP may be large because the performance curve from manufacturer is typically not able to fully reflect the transient feature in the water-cooled VRF startup procedure or the unstable operation of the system at the break time. 4.3.2. Comparison results of total hourly data Fig. 7 shows that about 81% of all the measured data fall within ±25% of the simulated data for cooling capacity. The mean of the absolute value of the hourly relative error for the total simulation period is 15.6%. Fig. 8 shows that about 64% or 83% of all the measured data for compressor power fall within ±30% or ±35% of
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8
100%
+35% +30%
80%
Pc (kwh)
6 60%
|RE|
5 40%
4 20%
3 2
Measured compressor power (w)
10000
Simulated data Measured data
7
11000
10:00-11:00 12:00-13:00 14:00-15:00 16:00-17:00
9000 8000
y=x
7000 6000
-30% -35%
5000 4000 3000 2000 1000
Time
1000
2000
3000
4000
5000
6000
7000
8000
9000
Simulated compressor power (W)
Fig. 5. Comparison between measured and simulated values of compressor power on last day.
Fig. 8. Hourly comparison results of compressor power.
100%
7 Simulated data Measured data
6
9
+20%
80%
y=x 8
5
40%
3 20%
2
7
Measured COP
4
|RE|
COP
60%
-20%
6
5
1 10:00-11:00
12:00-13:00 14:00-15:00
16:00-17:00
4
Time Fig. 6. Comparison between measured and simulated values of COP on last day.
3 3
4
5
6
7
8
9
Simulated COP 45000
Fig. 9. Hourly comparison results of COP.
25%
Measured cooling capacity (w)
40000
y=x 35000 30000 25000
-25%
20000 15000 10000 5000 5000
10000
15000
20000
25000
30000
35000
40000
Simulated cooling capacity (W) Fig. 7. Hourly comparison results of cooling capacity.
the simulated data respectively. The average hourly relative error for the total simulation period is 23.0%. Fig. 9 shows that for COP about 79% of all the measured data fall within ±20% of the simulated data. The mean of the absolute value of the hourly relative error for the total simulation period is 12.3%. In most hours, the simulated COP is higher than the measured one.
For a building energy simulation program, not all the hourly simulation errors are very small which is mainly caused by some uncertain and uncontrolled changes of the real internal loads and the outdoor weather conditions between hours. Much past research and simulation reports show some similar results. In Ref. [20], the simulation result of Energyplus also shows that the hourly difference of power between simulation and measurement can achieve 50% or even more which is mainly caused by the inaccuracy of hourly weather data. In Ref. [21], in some validation example for DOE, the difference of hourly total energy consumption between simulation and measurement can achieve more than 30%. So it is costly and hard to do a very accurate hourly validation for a complicated energy simulation model. When the period of comparison is longer, the average influence of all the uncertain factors will always become reduced. So the computed results are mainly in better agreement for longer time periods (Ref. [21]). In Ref. [20], there is a better agreement between simulated and measured data on a monthly basis. In Ref. [21], the results also show that the simulation error of monthly or annual is smaller than the one of hourly data. Consequently, the period of validation comparison is normally longer than an hour (use daily data or monthly data).
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320
100%
7
80%
6
Measured data Simulated data
Measured data Simulated data
280 240
5
160 40%
120
COP
60%
|RE|
Qc (Kwh)
200
4 3
80 20%
2
40 0 0
2
4
6
8
1
10
Day
0 0
Fig. 10. Measured and simulated daily cooling capacity.
1
2
3
4
5
6
7
8
9
10
Day Fig. 12. Measured and simulated daily COP.
50
100% 80%
40
60%
30
Measured data Simulated data
20
40%
|RE|
Pc (kwh)
Table 3 Correlation of simulated and measured data. Items
Cooling capacity
Compressor power
Correlation coefficient (R) statistic of t-test (t) Sample size (n)
0.861 4.48
0.909 5.78 9
4.3.4. Statistical and correlation analysis 20%
10
(1) Statistical and correlation analysis between simulated and measured values.
0 0
2
4
6
8
10
Day Fig. 11. Measured and simulated daily compressor power.
4.3.3. Daily profile and relative error In the research of building energy simulation, one day is normally the basic unit for the annual or monthly simulation and analysis. Figs. 10 and 11 present the comparison between measurements and simulated results of cooling capacity and compressor power consumption respectively. The greatest absolute value of the daily relative error of compressor power achieves 33.8%. The mean of the absolute value of the daily relative error of the 9 days is 15.7%. For the cooling capacity, the maximum absolute value of the daily relative error achieves 26.4%. The mean of the 9 days is 11.3%, a little lower than that of the compressor power. Although the error value is not the same in all the days, both the simulated cooling capacity and the simulated compressor power have similar evolvement profiles to the measured data. Fig. 12 gives the comparison of daily COP. The mean of the absolute value of the daily relative error is 7.60% while the maximum is 18.1%. The absolute value of the relative error in 6 days is less than 10%, and in 4 days is less than 5%. Only in one day it is bigger than 15%. Simulated COP is higher than measured one, that is, the performance of simulation is a little better than the real one. Under a real condition there are many factors that can cause bad influences on the system, and it is difficult to take into account all these factors in the simulation. Normally, the simulation model can be more correct in the stable and normal working conditions.
The statistical results can give the prediction of a sample population. To find the relations between measured cooling capacity (compressor power) and simulated cooling capacity (compressor power), Table 3 gives the correlation analysis of the daily data. The correlation coefficient can give the consistency and dependency between the simulation and experiment. The calculation equation of correlation coefficient is as follow:
Pn i¼1 ðxsi xs Þðxmi xm Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P Pn n 2 2 i¼1 ðxsi xs Þ i¼1 ðxmi xm Þ
ð3Þ
As presented in Table 3, both of the correlation coefficients are more than 0.85, which indicates high correlation between the simulated and measured values. The correlation hypotheses are verified through a t-test in a confidence level of 95% (t > t a=2 ðn 2Þ ¼ 2:36). The hypotheses of significant correlation are accepted. Cv(RMSE), a value of normalized measure of variability between simulated and measured data, is defined as the following equation:
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Pn 2 i¼1
C v ðRMSEÞ ¼
ðxs xm Þ n
xs
ð4Þ
The values of Cv(RMSE) in Table 4 for cooling capacity and COP are less than 15% in the simulation period of 9 test days, which reflects a small variability between simulation and measurement. The value of Cv(RMSE) for compressor power is a little bigger. (2) Statistical and correlation analysis of relative error Table 5 gives the standard deviation of the absolute value of the daily relative error for cooling capacity, compressor power and
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Table 4 Values of Cv(RMSE).
5. Conclusions
Items
Cv(RMSE) (%)
Cooling capacity Compressor power COP
14.5 20.4 9.9
Table 5 Standard deviation of the absolute value of relative error. Item
r (%)
Cooling capacity Compressor power COP
8.4 12.1 5.5
Table 6 Relation between the daily relative error of cooling capacity and that of compressor power. Item
Value
Sample size (n) Correlation coefficient (R) Statistic of t-test (t)
9 0.897 5.36
COP. All the standard deviations of the absolute value of the relative error are less than 15%, which can verify the small deviation from the arithmetic mean of the absolute values of the relative error.
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 Pn 2 1 i¼1 jREji n i¼1 jREji r¼ n1
ð5Þ
Same as the air-cooled model [16], there are two kinds of sequential factors affecting the last validation results of the water-cooled VRF simulation model. The first one is the accuracy of the EnergyPlus simulation model itself which mainly includes the correctness of all the input parameters in the input file. The second is the correctness of the modeling strategy and algorithm of the new module for the water-cooled VRF system. In this simulation, the cooling capacity is calculated and its accuracy is mainly decided by the original engine of EnergyPlus (correctness of input file). At the same time, the simulation accuracy of cooling capacity can affect the one of compressor power. Consequently, it is instructive to analyze the relation between the simulation error of cooling capacity and the one of compressor power. Table 6 gives the relations between the daily relative error of cooling capacity and that of compressor power. The similar results can also be seen in all hourly data. The hypotheses of significant linear correlation between the daily relative error of cooling capacity and that of compressor power are checked up and accepted using t-test in a confidence level of 95% t > t a=2 ðn 2Þ ¼ 2:36 . The significant linear correlation of the two variables can verify the strong dependency between simulation accuracy of compressor power and that of cooling capacity. Because the value of correlation coefficient is bigger than 0 (0 < R < 1), which means positive correlation, if the daily relative error of cooling capacity increases/ decreases, the one of compressor power will increase/decrease too. In other words, the simulation accuracy of the compressor power (mainly decided by the new simulation module) can track the accuracy of cooling capacity (mainly decided by the input file of EnergyPlus).
In this study, the water-cooled VRF simulation module is developed in the building energy analysis program, EnergyPlus, and validated by experimental data. The computer simulation of the water-cooled VRF air-conditioning system agrees with the monitored performance within generally acceptable accuracy. The main findings obtained from this work are presented as follows. (1) About 81% of all the measured data fall within ±25% of the simulated data for cooling capacity. The mean of the absolute value of the hourly relative error for the total simulation period is 15.6%. About 64% or 83% of all the measured data for compressor power fall within ±30% or ±35% of the simulated data respectively. The average hourly relative error for the total simulation period is 23.0%. (2) The greatest absolute value of the daily relative error of compressor power achieves 33.8%. The mean of the absolute value of the daily relative error of the 9 days is 15.7%. For the cooling capacity, the maximum absolute value of the daily relative error achieves 26.4%, and the mean of that of the 9 days is 11.3%. (3) The correlation coefficients between simulated cooling capacity (compressor power) and measured cooling capacity (compressor power) are more than 0.85, which reflects that high correlation exists between the simulated and measured data. (4) The simulation accuracy of the compressor power can track the accuracy of cooling capacity. In a word, many factors, which include the ideal assumption made in the new simulation model, the unstable and bad working conditions of experiment in some time, the measurement difference of all the devices and so on, can induce errors in the simulation. At the same time, a good description of the architecture in the input file is also an important factor leading to correct simulation results. That is to say, when the EnergyPlus model (input file) can reflect the real building load, the water-cooled VRF system module is able to reflect the actual compressor power with confidence.
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