NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Revisions to the SRCC Rating 

Process for Solar Water Heaters

Preprint

J. Burch

National Renewable Energy Laboratory

J. Huggins and S. Long

Solar Rating and Certification Corporation

J. Thornton

Thermal Energy System Specialists, Inc.

Presented at the 2012 World Renewable Energy Forum

Denver, Colorado

May 13-17, 2012

Conference Paper

NREL/CP-5500-54540

June 2012
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1

REVISIONS TO THE SRCC RATING PROCESS FOR SOLAR WATER HEATERS

Jay Burch

National Renewable Energy Laboratory

1617 Cole Blvd., MS52-02

Golden, CO 80401

e-mail: jay.burch@nrel.gov

Jim Huggins

Solar Rating And Certification Corporation

400 High Point Drive, Suite 400

Cocoa, Florida 32926

Jeff Thornton

Thermal Energy System Specialists, Inc.

22 North Carroll Street, Suite 370

Madison, Wisconsin 53703

Email: thornton@tess-inc.com

Steven Long, P.E.

Solar Rating and Certification Corporation

14001 Summit Dr.

Whittier, CA 90602

e-mail: stevenlong1@charter.net

ABSTRACT

In the United States, annual performance ratings for solar 

water heaters are computed with component-based simulation 

models driven by typical meteorological year weather and 

specified water draw. Changes in the process are being 

implemented to enhance credibility through increased 

transparency and accuracy. Changes to the process include 

using a graphical rather than text-based model-building tool, 

performing analytical tests on all components and systems, 

checking energy balances on every component, loop, and 

system at every time step, comparing the results to detect

outliers and potential errors, and documenting the modeling 

process in detail. Examples of changes in ratings are shown, 

along with analytical and comparative testing results.

Keywords: solar water heaters, ratings, solar water heater 

models, analytical testing.

1. INTRODUCTION

Annual performance ratings for solar water heaters (SWHs) at 

hundreds of sites across the United States are issued by the 

Solar Rating & Certification Corporation (SRCC) under its 

OG300 Guidelines (1).1

 1 The SRCC “one-day” ratings are not of concern here, and are not 

discussed further.

The ratings give the solar system 

annual savings relative to a standard conventional system, 

under specific draws and weather from typical meteorological 

years (TMY3, (2)). As in Fig. 1, there are two rating paths (3):

i) component test path; and ii) system test path. As indicated in 

Fig. 1, both paths yield calibrated simulation models that are 

used to generate ratings. In the component path, the key 

components (collectors (4), tanks, and heat exchangers (5), at 

the least) are tested according to standards yielding the 

input parameters for corresponding validated literal 

component-based models. A literal SWH model has a

one-to-one correspondence to the real system, by 

components and hookups. A literal model must exist for 

the component path to make sense. Ideally, the model is

validated over the range of rated systems sizes, and 

analytically tested for input errors. Actual systems are not 

tested (keeping rating costs low). The component path is 

very similar to ISO 9459-Part 4 (6), with the key 

exception that in the ISO standard the system must be 

physically assembled and tested to validate the system

model. This makes the ISO component path expensive.

If a validated literal system model does not exist (e.g., as 

for various self-pumping systems) or any component

model is not well-validated with prescribed tests for 

inputs (e.g., a heat pump water heater), the component

path cannot be used. In this case, the system as a whole 

must be tested. A model that is “close” to the system is 

then fit to the system test data, yielding an “effective”

model. A thermosiphon system, for example, might be 

chosen to model a self-pumping system, which is then 

calibrated to the data. This path is very similar to ISO 

9459, part 5 (7), where a highly-simple model is used for 

all active systems. System path models result in

“effective” models, with the regressed parameters 

subsuming the modeling mismatches in unknown ways. 

As a result, models from system tests cannot be easily 

extrapolated to similar systems of different sizes. In this 

paper we focus on the component path, and do not discuss 

the system path further.
2 

Fig. 1. Two paths available for rating: component tests and 

system tests, both followed by simulation of the ratings. 

SRCC has stated a desire that ratings be accurate to better than 

10%, although there is really no clear definition of accuracy, 

nor is there any process or database to verify that desire. 

Accuracy of the ratings is a basic issue for all stakeholders. 

Stakeholders, in increasing order of concern about accuracy of 

ratings, include: i) consumers, comparing performance for 

possible purchase of a unit; ii) analysts, comparing SWHs 

with other advanced technologies for policy and R&D 

planning; iii) incentive organizations, calculating 

performance-based rebates based on the ratings; iv); suppliers, 

comparing their SWHs to other SWHs and using ratings in 

sales; and v) rating organizations, such as SRCC and the 

International Association of Plumbing and Mechanical 

Officials, that generate and maintain the ratings. Incentive 

organizations and suppliers have the highest accuracy desires 

among users, as significant money hinges on the ratings. 

A rating system must be credible with all the stakeholders. 

Credibility is founded on accuracy and transparency. 

Accuracy should be consistent across all system types, with no 

biases toward any technology or location vs. others. It should 

be accurate enough for use by all stakeholders (e.g., error < 

10%). The modeling accuracy should be well-validated on a 

component and a system level. Without a systematic program 

of validation demonstrating accuracy, it is not possible to 

maintain rating credibility. The system should have inherent 

transparency, so that the process is open to scrutiny at varying 

levels, depending on stakeholder needs. This paper presents a 

new ratings modeling framework that enhances credibility 

through increased accuracy and transparency. 

We first lay out the existing SRCC component path process 

and its attendant problems. In section 3, we consider analytical 

and numerical validation generally, indicating the vast array of 

possible validation tests and what they mean. Validation (like 

“truth”) is an ambiguous, multi-faceted beast that cannot ever 

be tamed. In section 4, we present a proposed ratings 

procedure featuring high levels of validation and inherent 

transparency. In section 5 we give examples of early 

results from the system, and indicate the change in ratings 

from the old to the new framework. Lastly, conclusions 

and future efforts are given in Section 6. 

2. CURRENT SRCC RATING PROCESSES

Both the current and proposed ratings structures are based 

on the modeling environment TRNSYS (8), because it is 

modular, extensible, and well-developed. Modularity

means it allows any components to be hooked up with any 

other components; there are few hardwired connections, 

and system configurations are very easy to change 

(important as suppliers innovate). This modularity also 

means components can easily be wrongly connected. The 

tool, similar to MatLab and other equation solvers, is 

extensible, facilitating modeling of any component whose 

governing energy algorithms are known. It has a welldeveloped library of component models needed for 

modeling almost all SWHs and hydronic systems. 

2.1 Ratings framework 

The framework includes the model generation process 

and the input generation process. The draw must be 

specified, including the time profile and the mains 

temperature, as in (9). 

The deckwriter. SRCC/OG300 system ratings began in 

1991, when TRNSYS system models consisted of text 

files called “decks” specifying which components are 

present and how they are connected. The deck is a simple, 

flexible way to implement simple systems. Unfortunately, 

an SWH deck representing the relevant features of a 

functioning SWH can go beyond 30 pages. The prospect 

of creating and maintaining thousands of such decks was 

daunting. A deckwriter was coded to automatically write 

the decks, based on a brief list of system components and 

their parameters. A structured syntax for input/output 

variable names was constructed to automatically couple 

inputs and outputs. The deckwriter also facilitated 

changes in component models. Revised component model 

descriptions with their named input and outputs are 

written only once, in the deckwriter. The system has 

worked well, as models have been generated and 

maintained for ~20 years, with currently ~2000 SWHs in 

the database (most of the 2000 are variations in system 

size). 

Draw specification. The impacts on the annual ratings of 

the assumed draw profile and mains temperature 

functions m(t) and Tmains(t) are discussed in a companion 

paper (9). It is shown that the current SRCC Tmains 
3

algorithm leads to ~8% upward bias in performance, 

depending mainly on collector FrUl. The temperature bias does 

not change the rank order of technology savings, but updated 

algorithms are needed for incentivizers.

Basic model validation. When OG300 began and implemented 

Fig. 1, a few system types and a small number of basic 

components were thought to be needed. A glycol and a 

drainback system were tested at a university over a range of 

component sizes (10). Physical models for the systems were 

assembled in TRNSYS and validated. These (and literally 

thousands of other SWH tests) indicate that a literal,

component-based simulation model (using existing component 

models with key inputs derived from test data) adequately 

describes the system performance of conventional SWHs. The 

component and system modeling algorithms have proven to be 

sufficiently accurate. Having said this, testing for correct 

implementation of both the physical modeling and the input 

process remains a need.

2.2 Shortcomings of the current rating structure

Model validation. The two SWH configurations tested in (10) 

supported the premise that modeling can adequately represent 

SWH performance. We make two assumptions when

extrapolating to similar models using the same algorithms: i)

the model correctly formulates the relevant physics and solves 

the coupled equations; and ii) no errors are made in preparing 

the inputs. It is impractical to do actual tests on all 

configurations. No procedures were laid out for the current 

SRCC process as to when and how to test and check the 

models for input errors and coding bugs, and further validation 

was not done.

Transparency. Text files are a numbing mind-death when they 

go on for thousands of lines, as illustrated in Fig. 2. Parameter 

values are buried in the text. Combined with an unwieldy 

variable syntax, the model is inscrutable. As a result, even a 

simple examination of the component models or their 

couplings becomes very difficult. Transparency is lacking.

Documentation/consistency. The modeling assumptions have 

limited documentation and have not always been applied 

consistently. For example, tank losses in stratified auxiliary 

tanks with solar pre-heat were overestimated because of toofew nodes, causing underestimation of electric system savings

(11). A problem not with the rating process so much as with 

SRCC practice lies in what data were considered acceptable. 

Collectors are always formally tested under Standard100/

ISO9806, but tank and heat exchanger data, as required in (5), 

were not always provided. Work-arounds and simplifying 

assumptions follow that decision. Consequently there is 

varying and unknown accuracy in tank and heat exchanger 

component models. In addition, documentation of the process 

for deriving inputs is spotty or non-existent.

Fig. 2. The first 6 pgs of a TRNSYS deck. Extended text

files are inscrutable, as is this figure.

Internal checks. Simulation models pose tradeoffs 

between convergence tightness, time step, noding, and 

simulation speed. Internal checks were not always made, 

so relaxing criteria to speed computation time became 

risky and led to occasional energy imbalances. Checking 

the model’s energy balances catches these errors.

User error. It is difficult to ferret out user errors when 

there is limited checking of results. As an example of a 

user error, the lower deadband on controllers was set to 0, 

due to stability issues in one-day simulations, and was not 

reset for the annual runs. This implies that the solar loop 

pump is pumping inappropriately on warm nights, 

increasing the parasitics by 50%. Although not detectable 

in savings (it decreased savings only a few percent), 

comparison of pump on times between PV-powered and 

∆T-controlled pumps will catch such an error.

3. VALIDATION OF SWH MODELS: GENERAL

Validation of the underlying SWH models is essential to 

build credibility in the ratings. Validation is akin to 

“truth” in that it is uncertain what is precisely meant and 

it is certain one will never answer all the questions. 

However, its difficulty should not be a deterrent. Without 

validation, reasonable but unanswerable questions remain, 

even if the physical modeling basis is accepted.

Some general sources of error in simulation modeling are 

given in Table 1 (12). There are many levels of validation,

depending on the degree of control exercised over the 

possible sources of error in a simulation. For our case, the 

first two errors are eliminated by rating process 

definitions of weather and draw for ratings. As previously 

discussed, the physical algorithms are generally not in 

question, although physical tests are important for 

systems having novel features not previously wellvalidated.
4

TABLE 1. TYPES OF SIMULATION ERRORS1

Errors external to the model

Actual weather and use conditions vs. assumed

Actual properties/characteristics vs. assumed

User error in inputs

Errors internal to the model

Incorrect algorithms for energy mechanisms/ couplings

Incorrect solution of algorithms

Coding error

1. Adapted from (12).

There are three broad approaches to validation (12): i) 

analytical: the model results are compared to an analytical

solution of the coupled equations the model purports to solve; 

ii) comparative: the model results are compared to other 

simulations solving the same problem, or model results for 

similar systems are compared; and iii) empirical: the model

results are compared to measured system performance, using 

the measured weather and draws and measured component 

parameters. Empirical validation is the only way to test the 

accuracy of the assumed physical algorithms; the other tests 

assume the physical algorithms are correct, and test the 

solution and input process. It is part of a future international 

effort toward a validation standard as in (12) for building 

models. The analytical/empirical validation implemented here 

is a first step toward developing such a standard. Analytical 

and comparative tests uncover both internal and external error.

4. PROPOSED RATING PROCESS

The proposed structure is mostly unchanged from that 

presently used. The same modeling environment is used, the 

same- or very similar- component models are used, and the 

processes for deriving inputs are essentially the same. Changes 

were made to promote transparency and increase accuracy, as 

summarized in Table 2. Change in ratings is discussed in 

Section 5.

4.1 Rating process basics

Before modeling began, it was necessary to define a 

systematic simulation protocol, structure a database that 

enables maintenance of models and documents results of tests, 

and define the generic SWH configurations in the SRCC 

OG300 database that must be modeled.

Simulation protocol. A simulation protocol specifies how to 

develop rating models and their inputs. The processes will be 

documented here similar to the house simulation protocol in 

(13). The protocol document will be written on a general and 

technical level, to allow scrutiny appropriate to different 

stakeholders.

TABLE 2. RATING PROCESS CHANGES

Accuracy-related

New draw New Tmains and mdraw(t); 3 draw levels

↓Tolerance Tight energy balances maintained

↑Noding Correct heating elements placement

Energy 

balances

Key component, fluid loop, and system 

energy balance checked every time step

Transparency-related

Graphical 

modeling

Components and their interconnection are 

clear and visible

Traceability All component and system models are 

accessible; versioning and ratings history

Documented 

tests

Results of all analytical and numerical 

tests are accessible; versioning history

Ratings database. The ratings database will hold all 

models and test results. It will be modular, so that 

stakeholders can have access to the specific parts of 

interest, as appropriate. A component supplier (collector 

or tank manufacturer) has access to those files relating to 

his component. An OG300 system supplier has access to 

files related to his system. System suppliers will 

automatically generate the relevant parameter file as part 

of their OG300 application in the near future. Similarly, 

testing laboratories and manufacturers will use supplied 

software to translate their test data into model inputs.

Configurations. A configuration is a generic system 

model that explicitly defines all components, their 

interconnections, and the driving forces. A configuration 

class is all SWHs modeled by changing only parameter 

values in a given configuration. Configurations result 

from a unique choice of components and their 

interconnections. As of this writing, there are 130 

configurations in the database, and about 100 of the 

configurations have been completed and passed analytical 

testing. Each configuration produces annual ratings, oneday ratings, or validation test results, as set by a switch in 

the system include file.

4.2 Transparency features 

The new modeling is inherently transparent because the 

system construction is graphical, with linked icons 

representing discrete components. Fig. 3 shows the model 

for a flat plate glycol SWH with a solar tank and an 

electric auxiliary tank, an external solar loop heat 

exchanger, and a differential controller. Model 

components are icons and are easily grasped. Compare to 

Fig. 2. Couplings are also graphical; couplings between 

any input and output variables in any two components are

accessible.
5

Coll return pipe

DT controller

Coll supply pipe Coll loop 

pump

External 

hx

hx supply 

pipe

hx return 

pipe

Tank 

pump

mains pipe

Delivery 

diverter

Tank

diverter

Temper 

control

Temper 

control

Solar 

tank

T-valve

T-valve

Tank-toTank pipe

Thermostats

Upper

Lower

Elec Elements

Flat plate 

collector Auxil 

tank

To load

Fig. 3. Graphical representation of a flat plate SWH, showing interconnected icons. (Two tempering valves allow the 

simulated valve location to vary with auxiliary type.)

Structured parameter files. The modeling is structured for 

vetting of inputs by SWH and component suppliers. Each 

system model has two files: one that contains all the 

parameters peculiar to his system, and one that specifies the 

simulation internal parameters. Having a compact list, a

SWH supplier can check that all his system’s parameters

are entered correctly.

Tanks and heat exchangers should have test data submitted 

as in (5). In cases of tanks, model parameters are extracted 

by a regression on the data (e.g., tuning of immersed heat 

exchanger models, or collector/tank parameters). In others, 

the test results are the input themselves, such as the 

performance map required in (5). The data and the tools 

applied to that data will be archived in the database, 

documenting these input derivations. Any changes in the 

data will be tracked in a versioning scheme.

Supplier results. Results are available at any time step 

desired, but hourly, monthly, and annual intervals are 

useful. Hourly files rapidly grow large and must be limited 

to small samples. Hourly files might cause excess staff time 

responding to questions, as hourly data is quite complex. 

Monthly/annual files are of most interest, and will be 

provided to all suppliers for all their systems. Suppliers will 

examine monthly data for their systems, certainly, and they 

could use that data in sales tools. Fees could be charged for 

supplier access to more details, such as voluminous hourly 

or even one-minute files. The supplier could be furnished 

with an un-modifiable version of the model itself, for 

optimization of his system.

4.2 Accuracy checks

Internal coding errors and external user errors are tested for. 

No empirical tests are used here.

Analytical validation. Analytical tests will be done on each

component and each configuration. Examples of the tests 

done are listed in Table 3. Both components and whole 

systems are validated. The goal is that every component and 

every system is tested. Tests and their analysis will be 

automated. Analytical testing is facilitated by the validation 

port placed in the model, allowing any drivers for sun, 

ambient temperature, draw, etc.

Component tests were implemented without expectation of 

finding issues. Unfortunately, that was not entirely true, 

illustrating the power of these tests. Collector models failed 

the test that collector outlet temperature must approach the 

stagnation temperature at very low flows. This led to a 

revision of the theory for the heat removal factor appropriate 

for collectors modeled with a quadratic efficiency equation

(14). The Fr equations developed for a collector with linear 

efficiency equation were used with a quadratic collector 

curve. The error is small under normal operation, so no one 

had noticed the inconsistency.

TABLE 3. ANALYTICAL TEST EXAMPLES

Component tests

Coll. stagnation Very low flows ⇒ Tcoll,out → Tstag?

Coll. Tout Design flows, Tcoll,out = Tpredicted?

Tank decay Time constant = Mcp/UAtotal?

Temper. valve Given γ

1

, Tout, mout,1, mout,2= predicted?

Pump Given γ

1

, power & flow = predicted?

System tests

Constant draw mdraw, I, Tamb fixed; Tsysout=Tpredicted? 

Note 1: γ is the control signal to the component

System tests have not been developed much to date. A

simple case is shown in Table 3. This test does provide a 

fairly stringent test on the model. The system validation is 

assured mainly through implementation of energy balances.
6 

Energy balances. An energy balance proceeds by defining a 

system of interest, and checking how well energy 

conservation is obeyed: 

∑iQin,i + ∑iQout,i = ΔQstored 

Energy balances are done on components, all loops (such as 

a solar-heat exchanger-pump loop), and the system. When 

energy balances are tightly maintained, there is high 

confidence that the solution is proceeding correctly.

Component models were modified as necessary to output 

energy flows. All energy balances are checked at every time 

step. Time-step imbalances and their annual average and 

maximum value must be less than a specified tolerance. 

Configuration comparison (CC). The CC test compares all 

configurations, running a standard system in all 

configurations. The collector area/type/orientation, and tank 

volume are kept constant. Other parameters are adjusted to 

be as equivalent as possible. CC finds errors large compared 

to typical variations between configurations. It allows 

systematic differences between configurations to be seen, 

such as differences between drainback and glycol systems. 

Examples are shown below. The tool to do these 

comparisons may be made available on the SRCC web site. 

System intercomparisons. It is useful to intercompare the 

ratings to look for outliers and show how a given system 

competes with other systems of similar configuration. This 

test will be implemented when the ratings data base is fully 

populated. To account for system size and compare across 

climates, savings can be plotted versus a normalized 

variable, such as Hday,avgAcoll. Each supplier can be provided 

plots of his systems against all other similar systems. He can 

see if he is doing well, or needs to improve his design. 

5. INITIAL RESULTS 

Fig. 4 shows the CC test results for one system of each 

configuration for five cities: Phoenix, Washington DC, 

Denver, Chicago and Seattle. , indicating noticeably higher 

savings for some configurations. The data can be displayed 

via two independent filters. Fig. 5 shows savings for 

systems with tankless or boiler backup vs. all other systems. 

It is evident that the tankless/boiler auxiliary induces a large 

increase in savings. The reference is a standard gas storage 

tank water heater, implying that the solar savings is added 

with savings from a better auxiliary. Savings has to be 

understood as (solar+tankless) vs. gas storage water heater. 

Fig 6 shows the savings of all configurations with gas 

auxiliary, versus all configurations with electric auxiliary. It 

is evident that the gas case saves more than the electric case. 

Notice the difference is constant across configurations. The 

difference is due to two effects: i) the conversion efficiency 

ηgas,conv = ~0.8, whereas ηelec,conv ≡ 1.0, implying gas fuel 

saved for a given solar therm in the tank (which TRNSYS 

computes) is 25% higher; and ii) the electric tank bottom 

element is ~ 8” above the tank bottom, implying increased 

tank losses of order several hundred kWh with solar, which 

also decreases savings (11). These two effects are mostly 

independent of the solar system, so we expect the gaselectric difference to be mostly constant, independent of the 

solar pre-heat system, as shown. This is a severe test of 

modeling accuracy, which is passed by the proposed system. 

Fig. 4. Savings for a standard system in all configurations 

modeled to date. Note the grouping around 4,200 kWh. 

Fig. 5. The upper blue dots are all SWH with tankless 

auxiliary, and the lower red dots are all other SWH. 

A scatterplot showing agreement between the current and 

the proposed system is shown in Fig. 7. In general, there is 

consistent reduction in savings, averaging 8% for this subset 

of systems. Some systems show decreases up to 40%. There 

is no noticeable trend in percent rating change with savings 

level, as shown in Fig. 8. The main causes for the change in 

ratings are the draw changes (9), both in changing to mostly 

tempered draws and in increasing the mains water 

temperature by about 5 o

F. Other causes for the change in 

outliers have been investigated in a few cases, but it is 
7 

arduous to do so and won’t be done routinely. Energy 

balance errors and thermostat placement in current models 

were two problems identified. Part of the disagreement is 

caused by the differences between TMY2 and TMY3 

radiation data. Differences on total horizontal incidence are 

shown in Fig. 9. Beam radiation increased slightly, and 

diffuse radiation decreased. Although the differences are 

low on average (1%), some significant irradiance 

differences will show up in new-old comparisons. 

Fig. 6. Upper blue dots are for SWH with gas storage tank 

auxiliary, and lower red dots have electric storage tank 

auxiliary. 

0

1000

2000

3000

4000

5000

6000

7000

0 1000 2000 3000 4000 5000 6000 7000

Annual Energy Savings (kWh) - New Results

Annual Energy Savings (kWh) - Previous SRCC Results

Fig. 7. Agreement between savings for current and proposed 

ratings. 

6. CONCLUSIONS AND FUTURE WORK

The current SRCC ratings structure has functioned well for 

over 20 years, providing models for ~2000 rated systems in 

about a hundred configurations. However, its shortcomings 

in transparency and validation became apparent in several 

instances investigating rating accuracy, and a change was in 

order. 

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

0 1000 2000 3000 4000 5000 6000 7000

Percent Difference [(New - Old)/Old]

Old SRCC Rating [kWh Savings]

Fig. 8. Percent change in the ratings vs. the old SRCC 

rating. 

-15%

-10%

-5%

0%

5%

1

Total Horizontal Radiation - % Change TMY2 to TMY3

TMY2 Sites 1-241 are along the horizontal axis; they are not labeled

Fig. 9. Percent change in Hhoriz, between TMY2 and TMY3 

weather files; Δ ≡ (HTMY3 – HTMY2)/ HTMY2. 

A new rating structure has been developed and is in the 

process of being implemented. It is inherently transparent, 

as the components and couplings are graphical and models 

can be understood relatively easily. A ratings database is 

being constructed that embodies all the component and 

system models, and documents all the analytical and 

comparative testing done on the components and systems. A 

key aspect of the new models is a systematic application of 

energy balance checks on every component, every fluid 

loop, and the total system. The level of accuracy has been 

improved to allow consistent and meaningful crosscomparisons and checks. 

7. ACKNOWLEDGMENTS

This work was supported by the U.S. Department of Energy 

under Contract No. DE-AC36-08-G028308 with the 

National Renewable Energy Laboratory. The support of 

DOE managers Tony Bouza, Alexis Abramson, and 

Bahman Habibzadeh at DOE/BTP/Emerging Technology, 
8

and NREL managers Tim Merrigan, Ron Judkoff and Ren 

Anderson is gratefully acknowledged.

8. NOMENCLATURE

cp = specific heat

Symbols:

Fr = Heat removal factor

H = Solar incidence over some period (energy/area)

I = Solar irradiance (power/area)

m = mass flow rate

M = mass

Qsav = Energy saved 

T = Temperature

Ul = Collector loss coefficient

γ = control signal

η = Efficiency (collector or system)

∆ = Difference

amb = ambient condition

Subscripts

ann = annual, or annual average

col = collector

conv = input fuel conversion efficiency

elec = electric auxiliary “fuel”

gas = natural gas fuel

in = incoming

inc = incident solar on collector

mains = water mains inlet

out = outgoing

predict = predicted value

stored = stored energy with system boundary

BTP = Buildings Technology Program

Acronyms

CC = configuration comparison test

DOE = U.S. Department of Energy

SRCC = Solar Rating and Certification Corporation

SWH = solar water heater(s)

TMY = Typical meteorological year

TRNSYS= Transient system simulation

9. REFERENCES

(1) SRCC Operating Guidelines OG-300: Operating 

Guidelines and Minimum Standards for Certifying Solar 

Water Heating Systems, available at http://www.solarrating.org/certification/standards.html.

(2) TMY3. Description and manuals are available at: 

http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/

(3) Burch, Jay, J. Thornton, J. Huggins, and S. Long, 

“Simulation-based Ratings for Solar Hot Water Systems”, 

American Solar Energy Society, Annual conference 1993.

(4) ISO 9806: Test methods for solar collectors- Parts 1,2, 

& 3. Available from the International Organization for 

Standardization (ISO), www.iso.org.

(5) TM-1: Solar Domestic Hot Water System and 

Component Test Protocols Available from SRCC at 

http://solar-rating.org/certification/standards.html

(6) ISO 9459, Part 4. System performance characterization 

by means of component tests and computer simulation. 

Available from the International Organization for 

Standardization (ISO), www.iso.org

(7) ISO 9459, Part 5. System performance characterization 

by means of whole system tests and computer simulation. 

Available from the International Organization for 

Standardization (ISO), www.iso.org

(8) Sandy Klein et al. TRNSYS; A Transient System 

Simulation Program; Solar Energy Laboratory, University 

of Wisconsin-Madison: Madison, WI, 2006.

(9) Burch, Jay, and J. Thornton, “A Realistic Draw 

Specification for Rating Solar Water Heaters”, 

ASES/WREF2012, Denver, CO, May 13-19.

(10) Davidson, J.H., W. Carlson, and W. Duff, “Impact of 

Component Selection and Operation on Thermal Ratings of 

Drain-Back Solar Water Heaters,” _ASME J. Solar Energy 

Engineering, V114, 4, 219-226, 1992.

(11) Burch, Jay, “Deriving Simulation Parameters from 

Rating Data for Stratified Storage Tank Water Heaters”, 

American Solar Energy Society, Annual conference 2011. 

(12) Ron Judkoff, Wortman, D., O’Doherty, B. and Burch, 

J. “A Methodology for Validating Building Energy Analysis 

Simulations."Technical report NREL/TP-550-42059, April 

2008. 

(13) Hendron, R. and C. Engebrecht, Building America 

House Simulation Protocols, 2010, Golden, CO. NREL/TP550-49246.

(14) Thornton, J., private communication; to be published.