March, 2019, Vol.9

AHPNW
NEWSLETTERCONTENTS~Related News~
1.
A2L refrigerants in HVAC&R industry2.
Next-generation Refrigerant Initiatives~Research Papers~
3.
Application Analyse of a Ground Source Heat Pump System in a Nearly Zero
Energy Building in China

Related News

From Korea■

A2L refrigerants in HVAC&R
industry
As the industry has become moreadvanced, the CFC and HCFC refrigerants
used in HVAC&R industry have been
designated as regulated substances
because of global warming and destructionof the ozone layer. As a result, HFC
refrigerants, it has been used as the mainrefrigerant in the field of HVAC&R for morethan 15 years. However, HFC refrigerants
because ODP is '0', but GWP is high,causing serious problems in global warming.
Recently, HFC refrigerant preventing
regulations have spread worldwide, and inJune 2013, the European Parliament began
regulating the use of fluorine gas (F-gas) toprevent
climatechange.
Hydrofluorocarbons (HFCs) are scheduled
to be reduced by 16% compared to
December 2009. The agreement of theKigali Amendment Protocol on October 15,
2016, regulations on HFCs are being
implemented worldwide. When the EU
Regulation(517/2014)
has
been
implemented from 1 January 2015, a newequipment using HFC refrigerants with a
GWP of more than 2500 have been
regulated until 2020. Under the Kigali
Amendment, developed countries will
reduce HFC emissions use first, followed bya group of Article 5 countries including
China. India and nine other countries inSouth and West Asia will follow suit. Overall,
the agreement is expected to reduce HFC
use by 85 per cent by 2045. Countries are
divided in three groups, as per their phasedown schedules to freeze and reduce
production of HFCs. The developed
countries, led by the US and Europe, will
reduce HFC use by 85 per cent by 2036
over a 2011-13 baseline. China, which is
the largest producer of HFCs in the world,
will reduce HFC use by 80 per cent by 2045
over the 2020-22 baseline. India will reduce
the use of HFCs by 85 per cent over the2024-26 baseline. The amendment also
increases funding support to developing
countries. The HVAC&R industry are facing
to use lower GWP refrigerant. As a result, a
new refrigerant with a low GWP of purerefrigerant or mixed refrigerant has beendeveloped, which is called HFO (Hydro
Fluoro Olefins). Many of these products(some existing refrigerants such as R32
and ammonia) are characterized by low
flammability, and ASHRAE calls thisrefrigerant classification as A2L. Ammonia
is classified as B2L because of toxicity. In
order to expand the use of such A2L
refrigerants, the International Standard
Organization has recently revised the safety
standards of existing refrigerating and airconditioning equipment.
Currently, Korea does not have a clear
position on the Kigali Amendment, but inreality they cannot but ratify the revised
Protocol.
AccordingtotheKigaliAmendment Protocol, Korea belongs toGroup 1 of the A5, and will reduce HFC use
by 80 per cent by 2045 over the 2020-22
baseline.

The only answer is to use refrigerantsthat replace HFCs for these external
environmental
changes.
The
global
HVAC&R industry uses A2L refrigerants or
natural refrigerants, including HFO, as HFCalternative refrigerants. Europe, Japan, andthe United States, which are highly
regulated, already release new products
using A2L refrigerants, and have revisedand used safety standards to use theseRelated Newsrefrigerants. In particular, many of thestandards in the IEC 60335 series ofinternational electrical safety standards
have been recently amended to allow theuse of unflammable (A2L) refrigerants to
support the use of A2L refrigerants.
- IEC 60335-1:2010, Household and similarelectrical appliances - Safety - Part 1:
General requirements
IEC 60335-2-24:2010/AMD2:2017,
Particular requirements for refrigerating
appliances, ice-cream appliances andice-makers.
IEC60335-2-40:2018,
Particularrequirements for electrical heat pumps,
air-conditioners and dehumidifiersIEC
60335-2-89:2010/AMD2:2015,
Particular requirements for commercial
refrigerating
applianceswith
anincorporated or remote refrigerant unit orcompressor
These standards provide the conditionsof use of the A2L refrigerant to ensure
maximum safety during the application ofthis refrigerant. The main difference
between A1 refrigerants such as R-410A,
R-134a and R-407C and A2L refrigerants
such as R-32, HFO R-1234yf and HFO R1234ze is the velocity to propagate flames.
A2L refrigerant is burned but burning speed
is less than 10cm/s, which is lower than theburning rate of A3 refrigerant such as R-290
which actually explodes when ignited.
Actually, it is very difficult for A2L gas toignite, but precautions must be taken toprevent
accidental
accumulationofrefrigerant during system charging. The
manufacturer proposes to use an extraction
fan, especially when the outdoor unit is in
an enclosed space. International andEuropean safety standards, such as ISO
5149 and EN 378, provide a requirement tokeep flammability limits far below accidental
leaks.
As of January 1, 2017, all new cars
produced in Europe should only use
refrigerants with a GWP of less than 150 in

the air conditioning system. The current
available refrigerant is HFO R-1234yf. The
automotive
industryhas
conducted
thorough testing and risk assessment
before using R-1234yf to confirm that it is a
refrigerant to replace R-134a. R-32 (HFC
classed as A2L) is now widely sold as analternative to R-410A in new air
conditioning and heat pump systems
because of its performance similarity to R410A. Regulations for the use of A2L
refrigerants differ from those in Europe, but
in Japan many room air conditioners
already used R-32 refrigerants. Some large
chillers use R-1234ze instead of R-134a,
while R-1234ze is an HFO and is classified
as A2L but is actually a nonflammable
refrigerant at temperatures below 300°C.
Because R-1234yf is closer to theperformance of R-134a, the system issuitable for use in refrigeration systemdesigned to use low flammable refrigerants.
Because there is no flammability at room
temperature, R-1234ze is also used forsome aerosol applications. Currently,
refrigerant manufacturers are developing
A2L
HFO
blendswithalternativerefrigerants such as R-404A and R-410A.
Internationally, the use of low GWP
refrigerants according to the HFCs
refrigerant withdrawal scenario to prevent
global warming is a global trend.
Accordingly, the international standards forthe safety standard of the heat pump, theair conditioner and the dehumidifier havebeen revised to use the A2L refrigerant.
However, Korean safety standards still use
the old version which cannot use thisrefrigerant. Already, many countries suchas Europe, USA, and Japan have been
introducing products using A2L refrigerant
to the market and gradually expanding themarket.
Recently
Korea
National
TC(Technical Committee) are working tourgently revise the existing safety standards
so that A2L refrigerant can be used in
Korea by reviewing current international
standards. The revised versions will beavailable the middle of 2019. Afterwards, itis necessary to hasten to advance into

Related News
overseas market by expanding thedomestic market of A2L refrigerant products
and improving this technology by matchingdomestic KC (Korea Certificate) standards
with the latest version of IEC.

From Japan■Next-generation Refrigerant
Initiatives—Report on Kobe Symposium 2018
The International Symposium on New
Refrigerants and Environmental Technology
2018 (Kobe Symposium) was held at the
Kobe International Conference Center andorganized by the Japan Refrigeration and
Air Conditioning Indus- try Association(JRAIA). The 13th edition of thesymposium started on December 6 andended with success on December 7.
Around 550 people from Japan and aroundthe world participated in the symposium, aconference size second only to the 2016
symposium. Many timely topics werecovered with content significant to theindustry, indicating the high level of interest
worldwide in environmental and refrigerant
initiatives.
Many of the presentations at the symposium
focused on global warming countermeasures,with topics including new heating,ventilation,
air
conditioning,andrefrigeration ( HVAC&R) equipment andnew refrigerant technology development
contributing to environmental conservation
as well as the latest regulatory developments
in Japan and abroad.

The Kigali Amendment, which was adopted
at the 28th Meeting of the Parties to theMontreal Protocol (MOP28) in October
2016 in Kigali, Rwanda, came into force inJanuary 2019, and countries are now
working to meet their commitments under it.
The Kigali Amendment to the Montreal
Protocol was ratified by 60 parties as ofNovember 12, 2018, which triggered its
entry into force. While the obligations of therespective
parties
toimplement
environmental initiatives and the speed of
their implementation vary along with theirdifferent
political
andeconomic
circumstances, all parties recognize theurgency with which action on climatechange needs to be taken.
The symposium began with opening
remarks by Toshiyuki Takagi, chairman of
the board, JRAIA, and Masatoshi Omura,
executive director, Kobe Tourism Bureau.
Over the two days of the meeting,participants had the opportunity to attend awide range of technical sessions, poster
sessions, and presentations.

Related News(CRAA).
Appliance manufacturers talked about new
refrigerants during technical sessions 2 and3, and lectures on the safety of refrigerants
and risk assessments were delivered during
technical sessions 4 and 5. TechnicalSessions 6 and 7 covered compressors andlubricants. Speakers delivered presentations
on energy conservation in Technical Session
8. Finally, during Technical Session 9,
refrigerant manufacturers presented theirfindings on new refrigerants.
The session started with a keynote address
by Tetsuji Okada, president of JRAIA, titled‘History of the Kobe Symposium and theLatest Issues of the HVAC Industry, ’which covered the history of the symposium,
market trends, the latest developments inregulations and protocols, and global
environmental protection policy and efforts.

Technical
Session1
focusedonenvironmental issues. Toshio Kosuge from
Ministry of Economy, Trade and Industry(METI) discussed the amendment to Japan’s ozone layer protection law. Presentations
on
the
F-gas
Regulation,thehydrofluorocarbon(HFC)phasedown,
Ecodesign legislation, and safety standards
in Europe were made by Mihai Scumpieru
and Els Baert from the European
Partnership for Energy and the Environment(EPEE). Xudong Wang from the AirConditioning, Heating, and Refrigeration
Institute (AHRI) reported its research on
flammable refrigerants.
A presentation titled ‘ Updates onStandards Development and Revision in the
Chinese R&AC Industry Following theKigali Amendment ’ was made by
Huicheng Liu from the China Refrigeration
and Air Conditioning Industry Association

The sessions at the symposium gave updates
on topics covered at the Kobe Symposium2016 and on progress in refrigerant
development and HVAC&R technology,
among others. A2L refrigerant assessments
were the main topic discussed at the Kobe
Symposium 2016. For the 2018 edition, in
addition to A2L refrigerants, propane(R290) and other A3-class refrigerants werediscussed for the first time. Many presenters
reported on the results of their verification
experiments with these refrigerants. There
were also many reports on alternativerefrigerants to R410A and R404A. Other
presentations
discussed
equipment
development and experimental findings for
new low-global warming potential (GWP)
refrigerants that achieve high efficiency
such as R466A, R463A, and R448A. Issuesof stability and safety, namely flammability,

Related Newsfor new refrigerants are not yet settled
topics, and these refrigerants may ultimately
not prove to be the solutions the industry is
looking for. However, these newrefrigerants and the HVAC&R technicaldevelopments related to them do shine alight on global-scale environmental
initiatives and have the potential to lead tofuturedevelopments.
JARN had opportunities to interview many
lecturers individually during the symposium.
They include Stephen Kujak from Trane,
who gave a presentation titled, ‘Update on
Next Generation Low GWP Refrigerants for
Chiller Products,’ Masato Fukushima from
AGC, who talked about ‘Next Generation
Low-GWP Refrigerants AMOLEA,’ and
Dr. Sarah Kim from Arkema, who delivered
a lecture titled, ‘ Flammability and Risk
Assessment of Low Environmental Impact
Refrigerants for R134a and R404A
Replacement. ’ JARN plans to publish
these interviews in future issues.

In aninterview with JARN, Tetsuji Okada,president of JRAIA, commented, “ This
year ’ s Kobe Symposium recorded thesecond-highest attendance ever. Experts
from around the world praised theimpressive content offered at this
symposium. I intend to make future editionsof the Kobe Symposium a venue fordisseminating
informationonnewtechnologies related to refrigerants andcompressors.
”
The Kobe Symposium brings together
HVAC&R experts not only from Japan but
also from around the world to share thelatest information on refrigerant issues thatare some of the most important topics in theindustry today and as such, is set to attracteven more attention in the coming years.
(source : 2019/1/25,JARN)

Research Paper

From China

A new performance index for air-source heat pumps based on the nominal
output heating capacity and a related modeling study
Wei Wang, Yiming Cui, Yuying Sun, Xu Wu, Shiming Deng
Beijing University of Technology, Hong Kong Polytechnic University, China
Abstract
Air source heat pump (ASHP) units have been widely used for space heating in recent years.
While a space heating ASHP unit is normally rated at its nominal operating condition.
However, during its actual space heating operation, it rarely works at the nominal condition.
The actual output heating capacity can remarkably deviate from that at the nominal condition,
due to the influences of ambient air temperature and frosting-defrosting operation. Therefore,
to enable a comprehensive and convenient evaluation of the operating performance of ASHPs
with frosting-defrosting operation and to provide designers with appropriate design guidelines
to size a space heating ASHP unit, a new performance index has been proposed. The new
performance index, 𝜀NL , or the loss coefficient in the nominal output heating energy, wasactually based on the nominal output heating capacity of ASHP units, which was readily
available and stayed unchanged irrespective when and where ASHPs were operated. In thispaper, the defining of 𝜀NL was firstly given following a detailed explanation of the frostingdefrosting operation of an ASHP unit. Secondly, a GRNN model for predicting the 𝜀NL of afield ASHP unit was established following a correlation analysis using a total of 473 groupsof field measured data from a field ASHP unit. Finally, a modeling study using the developed
GRNN model was carried out, and the study results suggested that defrosting initiating time
would affect the 𝜀NL, and there may exist an optimal defrosting initiating time at which 𝜀NL
was at its minimum, and that an increase in ambient air relative humidity or a decrease inambient air temperature would result in an increase in 𝜀NL.
1 Introduction
Due to the advantages of high efficiency and environmental protection, air-source heat pump
(ASHP) units have been widely accepted all over the world [1]. Europe Union, Japan and
China successively identified the ASHP technology as one of the renewable energy utilization
technologies. The Department of Energy (DOE) in the US also regarded it as one of the mostpotential air conditioning technologies in the 21st century. Since the 1990s, ASHP units havebeen widely used for both space cooling and heating in cold, hot-summer and cold-winterregions in China [2-3]. Recently, coal-electricity conversion projects in an attempt to alleviate
severe air pollution in northern China as a result of using coal for space heating further
increased the scale of the applications of ASHP technology [4]. In 2017, 2.9 million numberof ASHP units were sold in China, representing an increase of 43.7% from that in 2016. It is
expected that the Chinese ASHP market will continue to grow at a yearly increasing rate ofmore than 20% over the next five years [5].
A space heating ASHP unit is usually rated at its nominal operating condition in terms ofoperating efficiency and output heating capacity. However, during its actual space heating
operation, it rarely works at the nominal condition [6-7]. Ambient air temperature andfrosting-defrosting can significantly affect the actual operating performance of ASHP units [6,8-10]. Fig. 1 shows the variation of the output heating capacity of an ASHP unit during actual
space heating operation [11-12]. At a non-frosting condition, the actual output heating
capacity decreases as ambient temperature decreases and is usually lower than its nominal
heating capacity. At a frosting condition, the actual output heating capacity further decreases
compared to that at the non-frosting condition, as a result of frosting-defrosting effect. It canbe seen from Fig.1 that at the frosting condition, the actual output heating capacity of the
ASHP unit can remarkably deviate from that at the nominal condition. Furthermore, different

7Research Paper

From China

frosting conditions, e.g., severe, moderate and mild, can also affect the actual output heating
capacity of an ASHP unit [13].
Building heating load / ASHP output
heating capacity(kW)Nominal condition

qnc
ASHP output heating capacity at nonfrosting condition
ASHP output heating capacity at
frosting condition

qw
Building heating load
ASHP’s operating point at
the design condition

Th

Tw

Ambient air temperature(℃)

qnc
Th

Nominal output heating capacity
qw Building heating load at the design(kW)
Outdoor air temperature at the
Tw condition(kW)Outdoor air design temperature for winteronominal condition ( C)space heating (oC)Fig. 1 Variations in the actual output heating capacity of a space heating ASHP unit/building
heating load with the changes in actual operating ambient air temperature

Various approaches have been used to evaluate the operating performances of ASHP units
during frosting and defrosting operations. Lu [14] and Shi et al. [15] used a heating season
performance factor, which reflected the operating efficiency of ASHP units during an entire
heating season. Ameen [16] and Jiang et al [17] evaluated the operating performances of
ASHP units during frosting and defrosting by using a loss coefficient of frosting-defrosting,which was defined as the ratio of the COP during a frosting operation to that during a nonfrosting operation at the same ambient air temperature. Zhu et al. [18] proposed to use anindex of heating efficiency to evaluate the operating performance of ASHP units during
frosting and defrosting, which was the ratio of the output heating capacity during a frosting
operation to that during a non-frosting operation at the same ambient air temperature. To
evaluate ASHPs’ operating performances, Li et al. [19] developed a generalized performancemodel for an ASHP unit in a single frosting-defrosting cycle and proposed to use system COP,
which was the ratio of the total output heating capacity to the total power input during thecomplete frosting-defrosting cycle, to evaluate its operating performance.
As seen, although there have been extensive research efforts in developing suitable indicatorsfor evaluating the operating performances of ASHP units, there were a number of
inadequacies in assessing the operating performances of ASHP units during a complete
frosting and defrosting cycle when using these indicators. Firstly, certain evaluation methods[14-15,19] only looked at the actual operating efficiency of an ASHP, without considering theloss in operating performances due to frosting and defrosting. Secondly, other methods [1617] only took the frosting operation in a frosting-defrosting cycle into account, without
considering the defrosting operation. Thirdly, although a number of evaluation indexes
considered the ratio of heating capacity/COP during the frosting operation to those during thecorresponding non-frosting operation at the same ambient air temperature [16-18], the actual
output heating capacity or COP during the corresponding non-frosting operation werevariable and thus difficult to obtain, so that these evaluation indexes were hardly applied topractice. On the other hand, in most previous studies, only the frosting-defrosting
performances of ASHP units at certain typical operating conditions were assessed by fieldtests. The performances at all frosting-defrosting operating conditions were however difficult

8

Research Paper

From China

to be evaluated using field tests due to high project cost and long project duration involved.
Furthermore, no previously developed performance indicators were based on the performanceloss against that at nominal conditions, leading to potential system oversizing, as the sizing of
ASHPs was usually based on the performance data at the nominal condition provided by
ASHPs’ manufacturers.
Therefore, to comprehensively and conveniently evaluate the frosting-defrosting
performances of ASHPs and to provide ASHP systems designers with an appropriate design
guideline regarding the actual operating performances during frosting and defrosting, a loss
coefficient in nominal output heating energy as a new frosting-defrosting performance
evaluation index for space heating ASHP units has been proposed. In this paper, firstly, adetailed account of the frosting-defrosting operation of an ASHP and the definition of the
proposed loss coefficient are given. Secondly, the development of a Generalized Regression
Neural Network (GRNN) based mathematical model for predicting the proposed loss
coefficient is reported. Thirdly, a modeling study using the developed GRNN based model ispresented. Finally, a conclusion is given.
2 The operating performances of an ASHP unit during frosting-defrosting operation
Fig. 2 conceptually shows the frosting-defrosting operation, with only 2 cycles, of a spaceheating ASHP unit. As seen, a complete frosting-defrosting cycle is made of a frosting
operation and a defrosting operation, and there can be a number of frosting-defrosting cycles
in a heating operation of several hours. The operating performances of an ASHP in terms of
its output heating capacity during a frosting-defrosting cycle are influenced by:
1) Ambient air temperature
As shown in Fig. 2, at a non-frosting operation, when actual operating ambient air
temperature is lower than the ambient air temperature at the nominal condition, the actual
output heating capacity (q hc1) is lower than nominal heating capacity (q nc ). The difference
between q hc1 and q nc is due to the difference between ambient air temperature at the nominal
condition and the actual operating ambient air temperature.
2) Frosting-defrosting operation
As also shown in Fig. 2, a complete frosting-defrosting cycle for an ASHP unit includes a
frosting operation and a defrosting operation. In the frosting operation having a duration of tf,
as the operation proceeds, the actual output heating capacity from the ASHP unit continues todecrease as frost continuously deposits on its outdoor coil surface. At t i , the ASHP unit starts
defrosting and enters the defrosting operation having a duration of t dc . The entire defrosting
operation may be further divided into a defrosting period and a heating restoration period, as
shown in Fig. 2. When the actual output heating capacity returns to 𝑞hc1 , the defrosting
operation ends and a new frosting-defrosting cycle starts.
Furthermore, the loss in the nominal heating capacity is closely related to when defrosting
starts. If defrosting starts earlier, the reduction in nominal heating capacity will be lower, butwith a shorter frosting operation. However, earlier defrosting leads to more frostingdefrosting cycles in a heating operation of a fixed time duration. On the contrary, if defrosting
starts later, the loss in nominal heating capacity during frosting operation will be greater, witha longer frosting operation and fewer frosting-defrosting cycles.

9Research Paper

Heating
capacity(kW)

Defrosting
tdc

Frosting
tf

tdf
qnc
qhc1
qhc2

From China

QF

Frosting
tf

trc

tdf

QF

QDF

qac
QS

trc
qnc
qhc2
QF
QS

trc

QDF

QS

0

ti
tf

Defrosting
tdc

tn

ti

Defrosting starting time (s)Frosting operation duration/defrosting
initiating time (s)Heating restoration period (s)Nominal output heating capacity (kW)Time (s)tn
tdf

Defrosting ending time (s)Defrosting period (s)

tdc
qhc1

Defrosting operation duration (s)Instantaneous output heating
capacity
at non-frosting
condition
Instantaneous
output heating(kW)
capacity before defrosting operation
Nominaldefrosting energy loss (kJ)(kW)

Instantaneous output heating capacityqac
at frosting condition (kW)Nominal frosting energy loss (kJ)
QDF
Effective heating energy (kJ)Fig. 2 Frosting-defrosting operations of a space heating ASHP unit

Based on the above analysis and Fig. 2, a loss coefficient in the nominal output heatingenergy, 𝜀NL, was proposed, as follows:
As shown in Fig. 2, a nominal frosting energy loss, 𝑄F , was defined as the loss in the nominal
heating energy during the frosting operation in a complete frosting-defrosting cycle, as
follows:
QF 

0  q  qdt
ti

n c

h c 2

(1)

The loss coefficient in the nominal heating energy during the frosting operation, εF , wasdefined as the ratio of the nominal frosting energy loss to the total available nominal output
heating energy during a complete frosting-defrosting cycle, as follows:
QF

F 

(2)

tn

 q dtnc

0

A nominal defrosting energy loss, QDF , was defined as the loss in the nominal heating energy
during the defrosting operation in a frosting-defrosting cycle, as follows:
QDF 

t  q  q dt
tn

nc

hc2

(3)

i

The loss coefficient in the nominal heating energy during defrosting operation, εDF , wasdefined as the ratio of the nominal defrosting energy loss to the total available nominal
heating energy during a complete frosting-defrosting cycle, as follows: DF 

QDF
tn

 q dt0(4)

ncThen, the loss coefficient in the nominal heating energy, εNL , in a complete frostingdefrosting cycle was defined as the sum of the loss coefficient in the nominal heating energy
during the frosting operation, εF , and that during the defrosting operation, εDF , as follows: NL   F   DF
(5)εNL was defined based on the nominal output heating capacity of an ASHP unit during acomplete frosting-defrosting cycle, and can, therefore, be used to comprehensively reflect the10Research Paper

From China

loss in the nominal heating energy during a complete frosting-defrosting cycle. Since thenominal heating capacity of an ASHP unit is readily available from its manufacturer, andwould not vary irrespective where and when the ASHP unit is operated, therefore, theoperating performances of the ASHP unit during a complete frosting-defrosting cycle can beevaluated comprehensively and conveniently.
3 Development of a GRNN model for predicting 𝛆𝐍𝐋With the above defined εNL for an ASHP unit, a comprehensive and convenient index toevaluate the operating performances of ASHP units with frosting-defrosting operation was
proposed. However, to comprehensively and conveniently evaluate the operating
performances of an ASHP unit in practice, it was necessary to obtain values of 𝜀NL under all
different ambient and operating conditions. Experimental/field studies can be employed toevaluate 𝜀NL for an ASHP unit, but it was difficult to get its performances at all frostingdefrosting operating conditions due to high project costs and long project duration involved.
Therefore, mathematical modeling approach for evaluating and predicting εNL values basedon limited number of experimental or field testing data was preferred. While there existed alarge number of modeling approaches, artificial neural network modeling has been one of themost powerful and popular tools for establishing mathematical models. Therefore, as one ofartificial neural network modeling methods, Generalized Regression Neural Network(GRNN) was adopted to evaluate and predict εNL values at different frosting-defrosting
conditions due to its advantages of faster training speed [20] and simple network architecture[21-22]. When developing the GRNN based model for evaluating and forecasting εNL in the
study reported in this paper, firstly, field tests were carried out from 2012 to 2016 heating
seasons using a field ASHP unit installed in Beijing, China, and 473 groups of field measured
performance data obtained. Then, through a correlation analysis, the main factors affecting the𝜀NL of the field ASHP unit were determined and used as the input parameters to the GRNN
model for predicting the 𝜀NL for field ASHP unit to be developed. Thirdly, the GRNN model
for predicting εNL with the determined input parameters was trained and tested using the 473
groups of field measured data. Finally, a modeling study using the developed GRNN modelon the impacts of varying ambient parameters and defrosting initiating time on εNL wascarried out.
3.1 Field test
3.1.1 Test setup
The field tests were conducted with a field ASHP unit serving an office building in Beijing,
China. There were 11 rooms in the office building with a total heating floor area of 185 m2.
Fig. 3 shows the schematics of the field test setup. As seen, the test setup consisted of the
field ASHP unit and a space heating system for the building. The field ASHP unit with aconstant speed compressor was employed as the heating source for the building. The nominal
heating capacity of the ASHP unit and power input to the ASHP unit were 14 kW and 4.3 kW,
respectively. On the other hand, the space heating system was made of fan coil units and hot
water circulating pumps, etc.
During 2012 to 2016 heating seasons when the field tests were carried out, various defrosting
initiating methods, such as Temperature-Time (T-T), Tube-Encircled Photoelectric Sensors(TEPS) [23] and Temperature-Humidity-Time (T-H-T) [24], for the field ASHP unit wereapplied, leading to its varied frosting operation durations. In addition, a measuring system wasinstalled to measure and record the testing ambient environmental conditions and theoperating parameters of the field ASHP unit. Table 1 shows the detailed information of themeasuring system, for the following parameters:

11

Research Paper

•
•
•
•

From China

Air side: ambient air temperature and relative humidity, outdoor coil air outlet
temperature and relative humidity, air side pressure drop across the outdoor coil;
Refrigerant side: temperature of the outdoor coil surface, temperatures and pressures atthe suction and discharge of compressor;
Water side: hot water temperatures at the inlet and outlet of the indoor coil in the field
ASHP unit, hot water mass flow rate;
Others: power inputs to the compressor, hot water pump and fan in the field ASHP unit,
mass of melted frost, etc.
W
F
T&RH

Supply water header

T

Outdoor
coil

∆P

T

Field
ASHP
unit

∆P
Expansion valve

Fan-coil units

Indoorcoil

Space heating
system for thebuilding

T

4-way
valve

W
P

T

T

P
Return water header
Hot water pumpCompressor
T: Temperature

F: Flow rate

RH: Relative humidity

W: Power

P: Pressure

ΔP: Pressure difference

Fig. 3 Schematics of the field test setup
Table 1 Details of the measuring /sensors devices
Sensors/devices
Number
Accuracy
Temperature sensor (PT1000)14±0.15 ºC
Temperature, humidity sensor

2±0.15 ºC；±5 %Pressure sensor
Power meter
Flow meter
Digital camera
Electronic scale

4
1
1
1
1

±0.4 %±1.0 %±0.5 %-±0.1 gFull scale−40~140 ºC−20~70 ºC；
0~100 %
0~40 bar；0~25 bar-0.5~10 m3 h−1
14 Megapixels
0~6000 g

3.1.2 Test cases and conditions
Data for training

100
90

Moderate frosting zone

80

RH (%)

Severe frosting zoneCondensing
region

70
60

Data for testing

Mild frosting zone




50
40
30

Non-frosting region

20
10
0
-15

-12

-9

-6

-3

0

3

6

9

Ta (oC)

Fig. 4 Ambient air states of the 473 groups of field measured data plotted on the frosting
map [13]

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The field studies using the field ASHP unit from 2012 to 2016 heating seasons resulted in a
total of 473 groups of field measured data. The ambient air temperature and relative humidity
at which the 473 groups of data were obtained are illustrated on a frosting map [13] shown in
Fig. 4. As seen, the ambient air temperature ranged from -4ºC to 7ºC and the relative humidityfrom 45% to 85%, located in the severe, moderate and mild frosting zones of the frosting map.
In addition, with various defrosting initiating methods applied, varied frosting durations of 10
to 85 minutes were resulted in.
The 473 groups of field measured data contained the following ambient and operating
parameters for the field ASHP unit, with their ranges and averages shown in Table 2.
Table 2 Details of the 473 groups of field measured data
Parameters
Range
Averageunit
Ambient air temperature
-4~7
1.5
ºC
𝑇a
Relative humidity
RH
45~85
68.7
%
Defrosting initiating time
10~85
34
min
𝑡f
Nominal frosting energy loss
0.15~15
3.6
kJ
𝑄F
Defrosting period
150~300226s𝑡𝑑𝑓
Nominal defrosting energy loss
3~6
4.2
kJ
𝑄DF
Instantaneous output heating
6.0~13.2
8.9
kW𝑞𝑎𝑐
capacity before defrosting
Mass operationof melted frost
0.1~2.8
0.79
kg
𝑀fw
Water supply temperature
38.6~41.1
39.8
ºC
𝑇ws
Water return temperature
36.2~38.9
37.6
ºC
𝑇wr
Loss coefficient in the nominal
13.0~48.1
33.4
%
𝜀NL
heating energy
3.2 Input parameters of the GRNN model
In order to determine the ambient and operating parameters affecting 𝜀NL of the field ASHP
unit, an analysis on the correlation between 𝜀NL and all the other parameters listed in Table 2
except QF and QDF based on the Pearson correlation coefficient (r) method [25] using the 473
groups of field measured data was conducted, and the analysis results are given in Table 3.
The reasons that QF and QDF were not included in the analysis were because they were related
to 𝜀NL.
Table 3 Correlation test results of the field measured data
Parameters
Pearson correlation coefficient (r)Ambient air temperature-0.45
𝑇a
Relative humidity
RH
0.35
Defrosting initiating time
-0.12
𝑡𝑓
Defrosting period
0.59
𝑡𝑑𝑓
Instantaneous output heating
-0.54
𝑞𝑎𝑐
capacity before defrosting
Mass operationof melted frost-0.143
𝑀fw
Water supply temperature
0.07
𝑇ws
Water return temperature
0.06
𝑇wr
The results shown in table 3 suggested that all these parameters were correlated with εNL , butwith varying degree of correlation. However, tdf, qac and Mfw may be considered as dependent
parameters as they were also affected by Ta, RH and tf, and therefore, they were not used as
the input parameters to the GRNN model. Furthermore, during the field tests, the variations in
Tws and Twr were relatively small at less than 7.5%, as compared to those of other parameters.
Therefore, the influences of water temperatures on εNL were small and the two parameters
were not used either as the input parameters to the GRNN model.

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Consequently, as shown in Equation (6), following the above analysis, Ta, RH and tf wereused as the input parameters to the GRNN model to be developed.
 NL  f (Ta , RH , t f )(6)
3.3 Training and testing of the GRNN model
The totally available 473 groups of field measured data were used for training and testing the
GRNN model. Since there were no specific guidelines available on allocating the percentageof the total data groups for either training or testing [26], and with reference to previous
studies using 75% [27] or 80% [28] of data groups for training, therefore, when developing
the GRNN model, a total of 362 data groups, or 76.5% of the total data groups, were used fortraining the GRNN model and the remaining 111 data groups, or 23.5%, for testing the
GRNN model. Furthermore, when allocating the data groups for training and testing, thedistributions of the ambient parameters on the frosting map [13] for both groups were similar.
3.3.1 Training of the GRNN model
As mentioned, when developing the GRNN model, Ta, RH and tf were assigned as the input
parameters to the GRNN model, and εNL as the output parameter. Fig. 5 shows the structureof the GRNN model for predicting εNL .
input
layer

patternlayersummation
layer

output
layer

Ta

x1

θ1

S1

y1

RH

x2

θ2

tf

x3

εNL

S2

θ362

Fig. 5 The structure of the GRNN model for predicting 𝜀NL
As seen there were four layers in the GRNN model, an input layer, a pattern layer, asummation layer and an output layer. The input layer was made of three simple distributed
neurons, which can directly pass the three input parameters, i.e., Ta, RH and tf, to the patternlayer, where there were 362 neurons representing the 362 groups of data for training. The
Gaussian function was used as the transfer function. In the summation layer, there were twodifferent kinds of neurons, one to sum the outputs from all the neurons in the pattern layer andthe other the weighted the outputs from all the neurons in the pattern layer. Finally, the output
layer consisted of only one neuron representing 𝜀NL.
Once data groups for training were determined, the weights for each neuron in the network
structure were also determined. A smooth factor, σ, which can be used to indicate theperformances of the GRNN model for predicting 𝜀NL, needed to be determined by using the
Cross Verification method [20, 29], based on the expected error percentage, EEP [20], shownin Equation (7).
  y  y 
362
i 1

EEP 



i

362
ymax

2

i

 100%(7)

Where, ŷi and yi are ith predicted and measured 𝜀NL values out of the total 362 data groups,
and ymax the maximum measured 𝜀NL value.

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An optimal σ value was obtained by using the Cross Verification method, with the following
steps:
1) Assuming an initial value for σ starting from 0.01, with an increment of 0.01up to 0.90.
2) Calculating the EEP values using Equation (7) for all assumed σ values from 0.01 to 0.90.
3) The optimal σ value was determined when EEP was at its minimum value.
Following the above procedures, the optimal σ value for the GRNN model was determined at
0.10, with the minimum EEP value of 3.45%. Consequently, the GRNN model developed
would have a strong regression ability for prediction, without however the possibility ofoverfitting, for a greater general application ability.
3.3.2 Testing of the GRNN model
After the training, the GRNN model was tested using the remaining 111 groups of fieldmeasured data, for verifying its learning ability and general application ability. To this end,
the EEP method as expressed in Equation (7), relative error (RE) method, mean absolute error(MAE) method and Pearson correlation coefficient (r) method were used as the evaluation
criteria [20] for the testing data groups.
RE, MAE and r were described as follows:RE 

y i  yi
yi

MAE 

100%

(8)

i 1111

1 111  yi  yi
111 i 1

(9)

In Equations (8) and (9), ŷi and yi are defined in the same way as in Equation (7).
The Pearson correlation coefficient, r, is an indicator of the degree of correlation for the
GRNN model developed. For the GRNN model structure for predicting 𝜀NL shown in Fig. 5,
its Pearson correlation coefficient is expressed as follows:
111

r







i

i





  y  y  y  y 
i 1



i

2

i



 

111 
 i1  yi  yi   i1  yi  yi 





(10)

2

111

Where, ŷi and yi are defined in the same way as in Equation (7), ŷi and yi the mean value ofŷi and yi , respectively.
50

45

+10%

Predicted εNL (%)

40

35

30

25

-10%

20
r=0.90
15
15

20

25

30
35
40
Field measured εNL (%)

45

50

Fig. 6 Comparisons between the predicted and field measured 𝜀NL using the GRNN model
based on the testing data of the 111 groups of field measured data15

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From China

Using the above methods, the comparisons between the predicted and field measured 𝜀NLvalues based on the testing data of the 111 groups of field measured data are shown in Fig. 6.
It can be seen that the correlation between the two was strong with an r value of 0.90, an EEP
value of 6.45%. 95.4% of the predicted 𝜀NL values were within ±10% error band, with an
MAE of 2.02%. These results suggested that the developed GRNN model was able to predictthe 𝜀NL with an acceptable accuracy.
4 A modeling study for the operating performances of an ASHP unit during frostingdefrosting using the GRNN model for predicting 𝜺𝐍𝐋With the availability of the GRNN model for predicting 𝜀NL, a modeling study was carried out
to investigate the operating performances of an ASHP unit in terms of 𝜀NL at different Ta, RH
and tf. The study results are presented in this Section.
4.1 Predicted 𝜺𝐍𝐋 values of an ASHP unit at different defrosting initiating time
At a constant ambient air condition, the influences of different defrost initiating time on 𝜀NL
are shown in Fig. 7. It can be seen that at a constant ambient air temperature of -3oC andrelative humidity of 70%, the predicted 𝜀NL was firstly decreased with a longer frosting period
or defrosting initiating time. The 𝜀NL reached its minimum value of 31.9%. Afterwards, 𝜀NL
was actually increased with an increase in tf.
36
Ta= -3 ℃ RH= 70 %Predicted εNL (%)

35

34

33

32

εNLmin=32.0%
31
20

25

30

35

40

45

50

55

60

tf (min)Fig. 7 Predicted εNL of an ASHP unit at different defrosting initiating time using the GRNN
model
The modeling study results clearly demonstrated that at a fixed operating ambient condition,different defrosting initiating time, as a result of using different defrosting initiating strategies,
would impact the operating performances of an ASHP in terms of 𝜀NL. As seen, there may
exist an optimal defrosting initiating time at which 𝜀NL would be at its minimum.
4.2 Predicted 𝜺𝐍𝐋 values of an ASHP unit at different ambient air conditions
The predicted influences of ambient air temperature and relative humidity on 𝜀NL using the
GRNN model are illustrated in Fig. 8. From Fig. 8(a), it can be seen that at a fixed ambient air
temperature of -3oC, the predicted 𝜀NL values were increased with an increase in relative
humidity, due to a higher faster frosting rate at a higher relative humidity. On the other hand,at each of the fixed ambient relative humidity, similar variation trend of decreasing-increasingin 𝜀NL to that shown in Fig. 7 also demonstrated.
Furthermore, as seen from Fig. 8(b), at a fixed relative humidity, the predicted εNL values
were increased with a decrease in ambient air temperature, mainly due to a lower ambient air
temperature. In addition, at each fixed ambient air temperature, similar variation of
decreasing-increasing in 𝜀NL to that shown in Fig. 7 was also demonstrated.

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36
63%
67%

35

64%
68%

65%
69%

-3℃
3℃

36

66%
70%

From China

-1℃
5℃

1℃
6℃

34

Predicted εNL (%)

Predicted εNL (%)

34

33
32

32
30
28

31

26
30

RH= 70%

Ta= -3 ℃

24
29
20

25

30

35

40

45

50

55

60

20

tf (min)

25

30

35

40
tf (min)

45

50

55

60

(a)
(b)
(a) Predicted 𝜀NL of an ASHP unit at varying relative humidity but a constant ambient air
temperature(b) Predicted 𝜀NL of an ASHP unit at varying ambient air temperature but a constant relative
humidity
Fig. 8 Predicted 𝜀NL of an ASHP unit at different ambient air conditions
5. Conclusions
In this paper, a new performance index for ASHPs with frosting and defrosting operations
based on the nominal output capacity has been proposed and a related modeling study carriedout. The following conclusions may be drawn:(1) To comprehensively and conveniently evaluate the actual operating performances of an
ASHP unit with frosting and defrosting operations, the loss coefficient in the nominal output
heating energy was proposed as the new performance index. The proposed loss coefficient
was actually based on the nominal output heating capacity of an ASHP unit, which was
readily available for the ASHP unit’s manufacturers and stayed unchanged irrespective when
and where the ASHP unit was used. This would therefore provide a comprehensive andconvenient way for evaluating the actual operating performances of ASHPs with frostingdefrosting operation and a guideline to system designer for properly sizing ASHP units.
(2) A detailed analysis of the 473 groups of field measured data for a field ASHP suggested
the proposed new index of loss coefficient in the nominal output heating energy, 𝜀NL, wasinfluenced by the ambient air temperature, relative humidity and the defrosting initiating time.
(3) A 4-layer GRNN model for predicting the 𝜀NL of the field ASHP unit was subsequently
developed, using ambient air temperature, relative humidity and defrosting initiating time as
input and 𝜀NL as the only output. 76.5% of the totally 473 groups of field measured data wereused for training the GRNN model and the remaining 23.5% for testing the GRNN model
established. The testing results demonstrated that the GRNN model for predicting 𝜀NL was ofan acceptable accuracy.
(4) A modeling study using the GRNN model was carried out to exam how varying ambient
air conditions and defrosting initiating time may influence 𝜀NL. The study results showed thatat a fixed ambient air temperature and relative humidity, increasing frosting duration ordelaying defrosting initiating time would lead to a decreasing-increasing variation trend in
𝜀NL, suggesting there may exist an optimal defrosting initiating time at which 𝜀NL would be atits minimum. On the other hand, at a fixed ambient air temperature, 𝜀NL would be increased
with an increase in ambient air relative humidity, due to a faster frosting rate at higher relative

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humidity. Furthermore, at a fixed relative humidity, 𝜀NL would be increased with a decrease inambient air temperature, as a result of a lower ambient air temperature.
Acknowledgements
This work was supported partially by the National Natural Science Foundation of China(Grant No. 51178015), National Twelfth Five-year Science & Technology Support Plan
(Grant No. 2011BAJ03B10), Training Plan of Top-notch Talent Youth, and the New-Star of
Science and Technology supported by Beijing City.
References
1
Qu ML, Xia L, Deng SM, Jiang YQ. A study of the reverse cycle defrosting performanceon a multi-circuit outdoor coil unit in an air source heat pump – Part I: Experiments.
Applied Energy, 91(1): 274-280, 2012.
2
Wang RZ, Zhang C, Zhai XQ. Discussion on the design elements of air source heatpump air-conditioning, heating and hot water system for residential uses. Chinese
Journal of Refrigeration Technology, (1): 32-41, 2014. [In Chinese]3
Ma YT, Dai BM. Analysis of air-source heat pump for room heating. Refrigeration andAir-conditioning, 13(7): 6-11, 2013. [In Chinese]4
Yue H, Li HY, Jiang Y. Realization of substituting coal with electricity and enhancing
the efficiency of electricity allocation by using air source heat pump. Energy of China,
38(11): 7-13, 2016. [In Chinese]5
Li CL. Hotspot Analysis of air source heat pump. Chinese Journal of Refrigeration
Technology, 38(S1): 78-84, 2018. [In Chinese]
6
Wang W, Liu JD, Sun YY, Wu X, Bai XX, Zhu JH. Key problems and countermeasures
for air-source heat pump under whole working conditions in Beijing. Journal of HV &AC, 47(1): 20-27, 2017. [In Chinese]
7
Bai XX, Sun YY, Wang W, Liu JD, Wu X. Field research on operating performance of
air source heat pump under low temperature conditions in Beijing. Building Science,
33(10): 53-59, 2017. [In Chinese]
8
Dong JK, Deng SM, Jiang YQ, Xia L, Yao Y. An experimental study on defrosting heat
supplies and energy consumptions during a reverse cycle defrost operation for an air
source heat pump. Applied Thermal Engineering, 37(5): 380-387, 2012.
9
Wang W, Xiao J, Guo QC, Lu WP, Feng YC. Field test investigation of thecharacteristics for the air source heat pump under two typical mal-defrost phenomena.
Applied Energy, 88(12): 4470-4480, 2011.
10 Song MJ, Deng SM, Pan DM, Mao N. An experimental study on the effects ofdownwards flowing of melted frost over a vertical multi-circuit outdoor coil in an air
source heat pump on defrosting performance during reverse cycle defrosting. Applied
Thermal Engineering, 67(1-2): 258-265, 2014.
11 ASHRAE Handbook: heating, ventilating, and air-conditioning systems and equipment.
ASHRAE Inc, 201212 Jan K, Peter C, Ari R. Heating and Cooling of Buildings Design for Efficiency. CRC
Press, 447-452, 2010.
13 Zhu JH, Sun YY, Wang W, Deng SM, Ge YJ, Li LT. Developing a new frosting map to
guide defrosting control for air-source heat pump units. Applied Thermal Engineering,90: 782-791, 2015.
14 Lu YQ. Design handbook for heating and air conditioning. Beijing: China Architecture& Building Press, 2008. [In Chinese]
15 Shi WX, Wang BL, Shao SQ. Design of small capacity air conditioners and heat pumps.
Beijing: China Architecture & Building Press, 2013. [In Chinese]18

Research Paper

16
17
18

19
20
21

22
23

24

25
26

27
28

29

From China

AMEEN FR. Study of frosting of heat pump evaporators. ASHRAE Trans, 99: 61-71,
1993.
Jiang YQ, Yao Y, Ma ZL. Calculation of the loss coefficient for frosting-defrosting of
air source heat pumps. Journal of HV & AC, 30(5): 24-26, 2004. [In Chinese]
Zhu JH, Sun YY, Wang W, Ge YJ, Li LT, Liu JD, Zhong AX. Field test investigation on
the mal-defrosting of air source heat pump under the typical condition of hot summer
and cold winter region. Building Science, 30(12): 15-19, 2014. [In Chinese]
Li N, Shi WX, Wang BL, Li XT. Performance model of general air source heat pump ina single frost /defrost cycle. Journal of Refrigeration, 36(2): 1-7, 2015.
SPECHT DF. A general regression neural network. IEEE Transactions on Neural
Networks, 2(6): 568-576, 1991.
Li Q, Meng QL, Cai JJ, Yoshino H, Mochida A. Predicting hourly cooling load in thebuilding: A comparison of support vector machine and different artificial neuralnetworks. Energy Conversion and Management, 50(1): 90-96, 2009.
Yu JM, Li M, Shu F. Application of GRNN-algorithm on load modeling of powersystem. Proceedings of the CSU-EPSA, 21(1): 104-107, 2009.
Ge YJ, Sun YY, Wang W, Zhu JH, Li LT, Liu JD. Field test study of a novel defrosting
control method for air-source heat pumps by applying tube encircled photoelectric
sensors. International Journal of Refrigeration, 66: 133-144, 2016.
Zhu JH, Sun YY, Wang W, Ge YJ, Li LT, Liu JD. A novel Temperature–Humidity–
Time defrosting control method based on a frosting map for air-source heat pumps.
International Journal of Refrigeration, 54: 45-54, 2015.
Akoglu H. User's guide to correlation coefficients. Turkish Journal of Emergency
Medicine, 18: 91-93, 2018.
Li N, Xia L, Deng SM, Xu XG, Chan MY. Dynamic modeling and control of a direct
expansion air conditioning system using artificial neural network. Applied Energy, 91(1):290-300, 2012.
Diaz G, Sen M, Yang KT, McClain RL. Simulation of heat exchanger performance byartificial neural networks. Int J HVAC Res, 5(3): 195–208, 1999.
Anderson CW, Hittle DC, Katz AD, Kretchmar RM. Synthesis of reinforcement learning,neural networks and PI control applied to a simulated heating coil. Artificial Intelligence
Eng, 11(4): 421–9, 1997.
Borra S, Ciaccio AD. Measuring the prediction error: A comparison of cross-validation,bootstrap and covariance penalty methods. Computational Statistics & Data Analysis,
54(12): 2976-2989, 2010.

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Asian Heat Pump Thermal Storage Technologies Network
To promote energy savings and combat global warming, there is an urgent need to spread efficientheat pump and thermal storage technologies on the demand side. Countries in Asia, which areenjoying rapid economic growth, should coordinate with one another to spread this technology.
Five to ten years from now, Asia will become a global economic powerhouse and heat pumptechnologies will play a considerable role in all sectors. Asian countries will therefore need toaddress common issues and problems that have already been faced in Europe and North America.
Concerning the building of connections and networks among countries, it is essential to share
information on diffusion policies, technology trends, applications, etc., and then to make incremental
improvements. Further, situations which can or should be handled through collaboration should be
handled flexibly, on a case-by-case basis, with the collaboration of all countries.
In order to encourage the use and development of heat pump and thermal storage technologies in
Asian countries we have established AHPNW in 2011.

Participating Countries and Entities
CHINA: China Academy of Building Research (CABR)INDIA: Indian Society of Heating, Refrigerating and Air Conditioning Engineers (ISHRAE)
JAPAN: Heat Pump and Thermal Storage Technology Center of Japan (HPTCJ)KOREA: Korea Testing Laboratory (KTL)VIETAM: Hanoi University of Science and Technology (HUST)THAILAND: King Mongkut’s University of Technology Thonburi (KMUTT)INDONESIA: Heating, Cooling & Thermo Fluids Technology Indonesia (HCTFTI)

Please visit our website for more information.
⇒http://www.hptcj.or.jp/e/ahpnw/tabid/571/language/en-US/Default.aspx

20