Conference Paper

NREL/CP-550-45465

April 2009

Diagnosis of Solar Water Heaters 

Using Solar Storage Tank Surface 

Temperature Data 

Preprint

J. Burch and L. Magnuson

National Renewable Energy Laboratory

G. Barker

Mountain Energy Partnership

M. Bullwinkel

State University of New York at Canton

To be presented at the American Solar Energy Society (ASES)

2009 National Solar Conference (SOLAR 2009)

Buffalo, New York

May 11–16, 2009
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DIAGNOSIS OF SOLAR WATER HEATERS 

USING SOLAR STORAGE TANK SURFACE TEMPERATURE DATA

Jay Burch and Lee Magnuson

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401

jay.burch@nrel.gov

Greg Barker

Mountain Energy Partnership

13900 N. 87th St.

Longmont, CO 80503

Matt Bullwinkel

State University of New York at Canton

34 Cornell Drive

Canton, NY 13617 

ABSTRACT

Based on the tank energy balance, net gain into the solar 

storage tank can be inferred from the time derivative of the 

average tank temperature. Using temperature at the tank 

wall under insulation as a surrogate for fluid temperature, 

sensor mounting is simple and the inferred gains are useful 

for solar water heater (SWH) diagnostics. Positive daytime 

gain is compared to solar gain computed from site-specific 

parameters for an assumed clear day. The solar storage tank 

loss coefficient is inferred from temperature decay at night, 

and is compared to the value computed from tank 

description. Larger draws are evident as sharp drops in 

storage temperature. Analyses are embodied in a tool 

validated against directly-measured gains. Inoperative

single-family SWH are easily detected, signaling need for 

repair. Detection of system control issues and shading are 

exemplified. In large multi-family systems, frequent daytime draws will bias the comparison to expected solar gain. 

1. INTRODUCTION

Diagnostic monitoring is taken here as determination from 

short-term data if a solar water heater (SWH) is operating 

correctly. To be routinely applied, it should be inexpensive 

and easy, especially if intended for small domestic SWH. 

Many diagnostic methods exist based on the wide range of 

possible data and allowed expense (1,2); we focus here on

improving a method based on the solar storage tank surface 

temperatures (3). Tank surface temperature can usually be 

easily accessed, and when insulated it is a good surrogate

for the nearby fluid temperature. Significant temperature 

rise on some days shows that a SWH is operating. 

Quantitative analysis using the tank as a calorimeter can 

indicate how well the entire system is performing, by 

comparing measured gain to expected operation. In (3), 

daily total net energy gain computed from the tank 

temperature rise was compared to gain computed for sitespecific parameters under no-draw, clear sky conditions. 

However, there are often daytime draws, lowering net gain 

and biasing the comparison. In this paper, the inferred net

gain and expected clear-day solar gains are compared 

dynamically as rates throughout the day. If draws are 

detected as low or negative dTtank/dt, then the comparison is 

not done using that period. This minimizes the effect of 

(unmonitored) draws on the comparison of net to expected 

solar gain, and increases the odds of getting some clearsky/no-draw conditions during the monitoring.

The work here is different from previous studies using tank 

surface temperatures (4,5). In those methods, a system 

characterization was derived that allowed accurate 

projection of annual performance. Complete weather data

including solar incidence is needed. The system must be 

isolated, with no draws. Here, weather data are not needed, 

and the system operates normally with draws. Compared to 

previous diagnostic methods (1,2), the method here is 

simple and low-cost. Compared to (2), this method has 

lower accuracy and diagnostic power. It can be implemented 

in controllers as a software option, notifying the owner 

when performance is sub-par. Another application is lowcost, large-scale screening via mail-in procedures, locating 

malfunctioning systems to be repaired and yielding overall 

reliability data on systems. Combined with subsequent 

repair data from partnering refurbishment firms, component 

failure rates could also be determined. The analysis

algorithms are developed in Section 2. The algorithms have 

been embodied in a spreadsheet available for download (6). 

The method is applied to both residential and multi-family 

SWH in Section 3, with conclusions in Section 4.

1
2. THEORETICAL BASIS

The “tank-as-calorimeter” method presented here is based 

upon the energy balance shown schematically in Fig. 1. 

With the control volume taken around the tank, the 

instantaneous energy balance is: 

dQtank/dt = ∑idQi/dt = dQsolar/dt – dQloss/dt – dQdraw/dt. (1)

where Qtank is given as CtankTtank,avg, and Ttank,avg is the 

capacitance-weighted average temperature, and Ctank is 

calculated as ρwtrcp,wtrVtank. Here, dTtank/dt is computed 

directly from differencing the tank temperature data. The 

choice of time step to compute the derivative is usercontrolled, useful to minimize noise when the data interval 

is perhaps too small. Tank loss is computed as 

dQtank,loss/dt = UAtank,est(Ttank,avg – Tenv). (2)

For a practical method, plumbing should not be breached. 

Here, the sensors are to be mounted on the surface of the 

tank, as indicated in Fig. 2. It is important that the sensor be 

under the tank wall insulation to keep its reading close to the 

tank fluid temperature. A simple resistor chain argument 

shows that the difference between the tank wall temperature 

and the tank fluid is typically < 0.1 o

C, sufficient for the 

modest accuracy of the method here. Surface-mount sensors 

compared well with immersed sensors in (5).

Tank Energy Balance

T

Qdraw

Qinternal

Qsolar

.

.

.

Qinternal = Qsolar – Qdraw – Qloss

. . . . Qinternal = Qsolar – Qdraw – Qloss. . . .

Qloss

.

Fig. 1. Schematic tank energy balance and surface 

temperature measurement.

In using the tank energy balance for calorimetry, the 

problem becomes how to separate the terms in the sum 

(Qsolar - Qloss - Qdraw). By taking dynamic data, times when a 

specific term is dominant can be separated in time, as 

illustrated in Fig. 3. Solar gains are evident as positive 

derivatives, and losses are evident at night. Draws are 

important, and it is clearly desirable to know when they are 

occurring. When the draw is significant compared to the 

tank’s thermal capacitance and the tank is well-mixed, the 

draw manifests as a sharp drop in Ttank,avg, as in Fig. 3 and in 

single-family data below. In most SWH in the U.S., flow 

rates on the tank side are large enough that the tank stays 

well-mixed. Draws can be undetectable at night when 

pumps are not mixing the tank. Draws will be often 

undetected in well-stratified, low-flow systems common in 

Europe, and the data may only show when the thermocline 

passes the sensor location. Draws can be undetectable in 

large multi-family systems where there are frequent draws 

that are in net smaller than the net solar gain into the tank.

Surface Mount Detail

Tank wall

Tank wall 

insulation

Temperature 

probe

Leads

Outer 

Tank wall jacket

Tank wall 

insulation

Temperature 

probe

Leads

Outer 

jacket

Fig. 2. The tank surface probe should be well-insulated. 

Table 1 shows expected accuracy of the flux inference. 

Accuracy of ~3% can be attained when Ttank,avg is accurately 

measured and Ctank is well-known (7). The key uncertainty 

is how well one or more surface temperatures represent 

Ttank,avg. In systems with high flow during solar operation

that are typical in U.S. design, the tank is turned over 

rapidly (Vtank/mdot,tank <~1/hr and is reasonably well-mixed. 

In this case, a single surface temperature represents the 

average to within a few o

C, implying about 10% accuracy at 

peak operation. Solar tanks can remain stratified during 

operation, however, as with low-flow systems in Europe 

where (Vtank/mdot,tank <~10 hr). Accuracy degrades severely

in this case unless 3 or more probes are used. 

TABLE 1. ACCURACY OF TANK CALORIMETRY 

Measurement δTtank,avg δQdot,tank/Qdot,tank

Line-averaging RTD 0.5 oC 3%

Surface temp, well-mixed 2 oC ~15%

Surface temp, stratified ~20 oC ~ 100%

3 Surface temp, stratified 2 oC ~15%

Temperatures on Sunny Day with draws

0:00 6:00 12:00 18:00 24:00

Ttank Solar gain

Draws

Standby 

losses

Fig. 3. Schematic tank temperature vs. time, showing 

periods of solar gain, draws, and standby losses.

2
2.1 Parameter inference

Assuming no draw, Eqn. 1 implies that solar gains are 

dQsolar/dt = dQtank/dt + dQloss/dt. Losses are computed with 

the computed value of UAtank. Pump on/off times are taken 

as the first/last positive dTtank/dt. The tank loss coefficient 

UAtank is inferred from the tank temperature decay at night, 

when no draws are expected. The tank UA is calculated 

from the well-known expression:

UAtank = Ctankln[(T(tbeg)–Tenv)/(T(tend)–Tenv )]/(tend–tbeg) (5)

where tbeg, tend are beginning and ending times of the decay. 

The user sets an invariant time window for analyzing decay. 

Tenv may be estimated, but it should be measured if more 

precision in inference is needed, as when one wants to 

detect night-time thermosiphoning. Tenv is particularly 

difficult to estimate when the tank is in a garage or other 

unconditioned space. 

2.2 Comparison of inferred parameters to expectation

Useful solar gain on a clear day is calculated as 

dQcoll,clr/dt=Acoll[Fr(τα)nKIAM(θ)Icoll.clr–FrUl(Tcoll,in–Tamb)] (4) 

where Fr(τα)n/FrUl are the measured collector gain/loss 

coefficients in the well-known collector efficiency equation. 

These values have to be corrected for off-test flow rate, 

series array configuration, piping losses, and heat exchanger 

penalty, as described in (7). These corrections are typically 

not large for small-scale systems, but can become important 

for large, multi-family systems, especially the piping loss 

term. An example is shown in Section 3.3. Table 2 lists 

inputs and data sources for the calculations used here.

Icoll,clr-day depends on site latitude, longitude, elevation, and 

collector orientation. Calculations here use the methods laid 

out in (8), based on correlations for beam and diffuse 

transmission. Correlation coefficients are gross space-time 

averages, limiting accuracy. Calculations have been checked 

against clear-sky data, and are considered accurate to about 

±10%. If Icoll is measured, that data could be used rather 

than the estimated clear-sky value. Tcoll,in is taken as Ttank,meas

when the tank is well-mixed. It can be hard to estimate in

stratified tanks when a single mid-tank probe is used (three 

probes mid/hi/low are best for stratified, low-flow cases, 

with the low sensor taken as Tcoll,in. Computation of Tmains-in

is provided, so formulae averaging it with the measured Ttank

can be tested. Tamb for calculating useful energy gain is 

generally not measured; in the tool here one can linearly 

interpolate Tamb from estimates of Tamb,max and Tamb,min. Tamb

data can be used directly if available. ρgrd is typically set to 

0.2 for grassy foreground or asphalt, 0.3 for concrete, and 

0.8 when fresh snow covers the ground. 

TABLE 2: ANALYSIS INPUTS

Symbol Parameter Definition Data Source

System/site/test Inputs 

Acoll Collector area Label1/Obs

2

Frτα Optical gain constant Label/SRCC3

FrUl Loss coefficient Label/SRCC

bo Constant in IAM formula Label/SRCC

Vtank Tank Volume Label/obs

Htank Tank Height Obs/spec

Rtank Tank insulation R value Specs/obsrv

θ Collector tilt & azimuth Obs

L Longitude Map

Lat Lat Map

Tamb,high/low Test lo/hi daytime temps Met data/obs

ρgrd Foreground reflectivity See text

Tamb,month,high/low Monthly average 

min/max ambient temp

Met data, 

local obs

Data reduction inputs

(dTtank/dt)min Minimum dTtank/dt for 

solar gain analysis

Trial and error

tbeg , ∆tUA UA analysis window Trial and error

∆Tmin Min. ∆T for UA analysis Trial and error

1) Label on the collector often lists collector parameters.

2) Obs = observation at the site

3) Solar Rating and Certification Corp., at www.solar-rating.org 

The expected values of pump on/off times are taken as the 

start/end of the period where dQcoll,clr/dt is positive. No 

deadbands are accounted for. Inferred values of UAtank are 

compared to expectation based upon 1-D calculation. Utank

is taken as kins/Lins. The tank surface area Atank is calculated 

based on inputs for Vtank and Htank assuming a cylindrical 

tank. Because of typical uninsulated thermal shorts (piping, 

supports), the actual U value is expected to be ~2X that of 

this simple estimate. Reverse thermosiphoning would show 

up as UA values much larger than this. It would be possible 

to gain further resolution of reverse thermosiphoning by 

valving off the collector for one night to establish the 

baseline for nighttime losses, then differencing that with 

results including the collectors. 

3. EXAMPLES: VALIDATION AND DIAGNOSTICS

The methods above have been embodied in a spreadsheet 

tool available for download (6). In this section, the tool is 

used to analyze data from three different projects. 

3.1 Building America test house: method validation 

Detailed data from a residence in Colorado are being 

acquired under the Building America Program (9), and those

data were used to validate the method. System 

3
instrumentation is shown in Fig. 4. Because solar gain was 

measured, the solar gain inferred from tank calorimetry can 

be compared directly. Measured Tenv and Tamb were used in 

the results shown here. Hourly data were used here. 

Boiler

Preheat Tank

Mains Water

F T

To

DHW Load

Solar Collectors

Auxilliary Tank

Controller

HX

Zone 1

Zone 2

Zone 3

Controller

Basement

F T

F = Flow Measurement

T

= Pump

= Temperature Sensor

= Solenoid Valve

F

T

T

T

T T

F

T

T

F

Ts

Ts

Fig. 4. Instrumentation schematic in the Solar Row #2.

Tank sensors were mounted under the insulation on the 

outside surface of the unpressurized pre-heat tank near 

bottom and top of the tank. The tank is well-mixed when the 

solar loop is operating, as shown in Fig. 5. When the solar

pump is off and there is no space heating or draw, the tank 

bottom decays more rapidly than the top of the tank. 

Possible causes include: convection currents off the tank 

sides/thermal shorts, ground thermal shorts at bottom 

supports, or small night thermosiphoning (although flow at 

night always read zero in the storage-side heat exchanger 

loop). When the pump is off and there is space heating, the 

top/bottom sensors converge, diverging again when the 

space heat is turned off. Similar behavior is indicated upon 

large draw. It is believed that this mixing is caused by 

apparently-strong convection currents set up when there is a 

large heat extraction from the tank. 

In Fig. 6, the predicted solar gain (yellow curve) is 

compared with the inferred solar gain (blue circles) over 

eight days corresponding to Fig. 5a. It can be seen that some 

days (2nd day), there are no measured points (indicative of 

irradiance too low to start pumps), and partly-cloudy days 

show gain below the clear-day values. Predictions vary in

peak height (e.g., clear-day curve for day 2 lower than clearday curve day 3) because the collector inlet temperature 

(taken as the lower of the tank sensors) varies, which causes 

efficiency and net solar to vary. The 5th and 6th days are 

clear days, and the model and measurement agree very well.

Measured solar gain is computed as flow-∆T across the 

tank-side heat exchanger loop. This value is compared with 

the inferred gain CtankdTtank/dt + dQloss/dt in Fig. 7. It can be 

seen that the correlation is good. For all data, the regression 

line for inferred gain CtankdTtankdt vs. direct measurement of 

the gain as flow*∆T has slope of .9 and R2 ~.8. 

Disagreement is expected due to neglect of system mass, 

undetected draws, and errors in tank loss calculations. These 

data validate that tank surface temperature data is 

sufficiently accurate for inferring fluxes in the tank. 

SR#2, Ttank top and bot

20

30

40

50

60

70

80

12/16/08

0:00

12/18/08

0:00

12/20/08

0:00

12/22/08

0:00

12/24/08

0:00

12/26/08

0:00

12/28/08

0:00

12/30/08

0:00

Temp [C]

Tank Top

Tank bottom

Draw flow

SpHtg Flow

Space htg Winter

SR#2, Ttank top and bot

30

40

50

60

70

80

8/12/08

0:00

8/14/08

0:00

8/16/08

0:00

8/18/08

0:00

8/20/08

0:00

8/22/08

0:00

8/24/08

0:00

Temp [C]

Tank Top

Tank bottom

Draw flow

Summer

Fig. 5. Summer (top) and winter (bottom) Ttank in Solar Row 

Unit #2. Draw and space heating flows are also shown.

0

500

1000

1500

2000

2500

3000

3500

4000

8/14 7:00

8/14 13:008/14 19:00

8/15 1:008/15 7:00

8/15 13:008/15 19:00

8/16 1:008/16 7:00

8/16 13:008/16 19:00

8/17 1:008/17 7:00

8/17 13:008/17 19:00

8/18 1:008/18 7:00

8/18 13:008/18 19:00

8/19 1:008/19 7:00

8/19 13:008/19 19:00

8/20 1:008/20 7:00

8/20 13:008/20 19:00

8/21 1:008/21 7:00

8/21 13:008/21 19:00

8/22 1:008/22 7:00

Q to Tank (Watts)

Energy Rate to Tank: Predicted

Energy Rate to Tank: Measured

Fig. 6. Predicted clear-day solar gain (yellow curve) vs. the 

measured solar gain (blue circles). 

UAtank is shown for the entire data set in Fig. 8. Measured 

Tenv data was used. It can be seen that during summer the 

inferred UAtank is quite close to the estimated value based 

upon a 1-D calculation, validating the UA inference. It is 

unusual to have agreement this close, due to un-insulated 

thermal shorts (piping/valving). Typically, the measured UA 

is ~2X the 1D estimation. The result here indicates that the 

thermal shorts are reasonably well-insulated. Note that 

during winter, the UA calculations will usually include 

periods with space heating load, so anomalously-large UA 

values result, as seen in Fig. 7.

4
Solar Gain (2 methods) vs. Time

0

3000

6000

9000

12000

13-Aug 15-Aug 17-Aug 19-Aug 21-Aug 23-Aug

Solar Gain [BTU/hr]

Qcoll as Flow*DThx

Qcoll as CDTtank

Fig. 7. Solar gain as measured by Ctank∆Ttank and as 

Flowhx∆Thx vs. time. Good agreement is seen.

3.2 SWH diagnostics in New York

Temperature and inferred flux for four days of data for a 64 

ft2 residential system are shown in Fig. 9. These data alone 

indicate functioning energy delivery: the unit warms up 

significantly on some days. Only two draws appear, both in 

the morning. Fig. 10 shows observed vs. calculated gain. 

Flux comparisons indicate that the unit is gathering most of 

the expected energy. Fig. 11 indicates inferred and 

calculated pump on/off times. The inferred start times on the 

first two days correspond reasonably to predicted values. 

Days 3 and 4 are evidently cloudy, but show shut-down 

close to expected times. By comparing rates, 38 data points 

were attained, as opposed to essentially 2 points using the 

daily total method in (3). With rate comparisons, good 

conclusions can be reached with only several days of data; 

one day of data under mostly-clear conditions is sufficient in 

the extreme.

Tank UA

0

2

4

6

8

10

12

14

16

18

20

22-Feb

12-Apr

1-Jun

21-Jul

9-Sep

29-Oct

18-Dec

6-Feb

(W/C)

Predicted Measured

Fig. 8. Tank UA on successive nights, with value during 

summer near the 1-D calculation. 

20

30

40

50

60

70

3/29 9:03

3/29 14:033/29 19:03

3/30 0:033/30 5:03

3/30 10:033/30 15:033/30 20:03

3/31 1:033/31 6:03

3/31 11:033/31 16:033/31 21:03

4/1 2:034/1 7:03

4/1 12:034/1 17:03

Temperature [C]

0

1000

2000

3000

4000

3/29 9:03

3/29 14:033/29 19:03

3/30 0:033/30 5:03

3/30 10:033/30 15:033/30 20:03

3/31 1:033/31 6:03

3/31 11:033/31 16:033/31 21:03

4/1 2:034/1 7:03

4/1 12:034/1 17:03

Q to Tank (Watts)

Energy Rate to Tank: Predicted

Energy Rate to Tank: Measured

Fig. 9. Temperature and solar flux (computed and 

measured) for 4 days for a small system in New York.

Measured vs. Predicted Solar Flux

y = 0.83x

0

1000

2000

3000

4000

0 1000 2000 3000 4000

Predicted Solar Flux (Watts)

Measured Solar Flux (Watts)

Fig. 10. Measured vs. predicted solar flux for a residential 

system in upstate New York.

Collector Pump Start and Stop Times

88

90

92

6 12 18

Time of Day (hours)

Julian Day (Day of Year)

Start (Measured)

Stop (Measured)

Start (Predicted)

Stop (Predicted)

 Fig. 11. Pump start/stop times, predicted and inferred.

5
A large SWH at Saranac Lake, NY on a 9-story multifamily building was monitored, with schematic in Fig. 12, 

and collectors and tank shown in Fig. 13. The system has 48 

4X8 collectors (Anet = 1368 ft2

) in 8 strings in series and up

to 8 collectors in a single string. The pressurized storage 

volume is 1389 gal. The piping run is about 250 ft. A 

schematic of the system is shown in Fig. 12. It is a glycol 

system with external doubly-pumped load-side heat 

exchanger. This case illustrates that the method applies to 

large-scale systems. However, there are additional issues 

with frequent, relatively-small daytime draws that reduce 

accuracy (unless draw data are taken), and piping runs in 

large systems that significantly impact losses.

∆ctrl

Heat exch.

Cold inlet

Hot outlet

Solar Storage Tank

Collectors

T

T

T

T

T

S

T

Fig. 12. Schematic of the Saranac Lake SWH. Temperatures 

are indicated by “T”, status with “S”. The red temperature 

sensor on right side of tank wall was used for calorimetry.

Collectors

Tank

Fig. 13. Saranac Lake SWH. Top: Tank in the basement, 

showing logger/sensor position. Bottom: one string of six 

collectors in parallel on the roof.

Collector gain and loss coefficients as printed on the

collector labels are given in Table 4. The multipliers for the 

off-test flow rate, series connections, piping, and the heat 

exchanger penalty (8) are also shown in the table. εhx was 

estimated at 0.5. The largest correction is a factor of 1.72 for 

piping. The corrections reduced the gain by 10% and 

increased the losses by 84%. The system corrections to 

collector parameters are especially important for large 

SWH. 

TABLE 4: CORRECTIONS TO SOLAR GAIN

Gain: 

orig.1 

Loss:

orig2 

Fflow1

[-] 

Farray

1

[-]

Fpipe1

loss

Fpipe1 

gain

Fhx1 Fgain:

corr1,3

Floss:

corr2,3

.781 .8 1.01 0.95 1.72 .995 .91 0.712 1.26

1) Unitless; label value

2) Units of Btu/hr-ft2

-

o

F; label value

3) Corr = Correction after applying the four factors.

Fig. 14 shows the tank temperatures and solar gains for an 8 

day stretch during Oct. 2008. The temperature data show a 

clear rise mid-day on four of the days. The corresponding

solar gain is shown as blue circles in the bottom part of Fig. 

14. It can be seen that the collected energy is ~60% below 

the expectation. Fig. 15 shows the pump start/stop times. 

The inferred values indicate late start-up each morning.

Piping/fluid mass warmup and consistent morning clouds in 

the cloudy area could both be factors.

10

20

30

40

10/13 0:5410/14 0:5410/15 0:5410/16 0:5410/17 0:5410/18 0:5410/19 0:5410/20 0:54

Temperature [C] 0

10000

20000

30000

40000

50000

60000

70000

80000

10/13 0:5410/14 0:5410/15 0:5410/16 0:5410/17 0:5410/18 0:5410/19 0:5410/20 0:54

Q to Tank (Watts)

Energy Rate to Tank: Predicted

Energy Rate to Tank: Measured

Temperature [C]

20 Qsolar [kW] 80

60

20

0

Fig. 14. Tank temperature and solar flux (computed and 

measured) for 8 days for a large system in New York.

Additional data channels can help to resolve uncertainties 

that arise from using a single sensor. The channels shown in 

Fig. 12 in black font were installed to help explain the 1-

channel calorimetry data, data shown in Fig. 16. The tank 

data clearly show that the tank is well-mixed once the heat 

exchanger pump turns on. This was surmised based upon 

the specified 50 gpm flow rate at the heat exchanger (~2.3 

turnovers/hr). The data also indicate that there were draws 

all day long, because Tmains never rose above 40 o

F. Only for 

~2-5 AM did Tmains show any rises toward Tenv. Draw 

energy lowers net energy, so its effect is indistinguishable 

from “low” Qdot,solar. There are few times without draws and 

they cannot be seen as discrete events, as occurs with draws 

in single-family data, reducing accuracy. With large, multifamily systems, draw flows should be monitored and 

subtracted out of the energy balance, if a valid comparison 

with the expected solar gain is desired. 

6
Collector Pump Start and Stop Times

255

260

265

270

0 2 4 6 8 10 12 14 16 18 20 22 24

Time of Day (hours)

Julian Day (Day of Year)

Start (Measured)

Stop (Measured)

Start (Predicted)

Stop (Predicted)

Fig. 15. Pump start/stop times (inferred and calculated) for 

the Saranac Lake multi-family SWH.

Fig. 16. Multi-channel data for one day on the multi-family 

Saranac Lake SWH.

3.3 SWH Field Testing using tank as calorimeter

Field monitoring was conducted on ten installations of a 

new SWH to detect and resolve any malfunctions (10). 

Inoperative systems were easily detected in two cases, as 

Ttank(t) never fluctuated more than a few o

C. The circulation 

system failures were identified and fixed, with appropriate 

changes in the system manuals. In cases where pumps run 

appropriately but there is no actual flow, one can be fooled 

into thinking the SWH is functioning properly. 

Fig. 17 shows data from a properly-operating system. The 

temperature data show that the system is collecting energy 

properly. However, the flux data in Fig. 17b shows that the 

system energy collection is quite low in morning. Fig. 18

shows that the pump operates as expected, pointing to

morning shading issue; this can also be seen directly in the 

temperature data in Fig. 17a, evident as the relatively slow

temperature rise in the mornings. 

20

30

40

50

60

5/5 23:485/6 23:475/7 23:455/8 23:405/9 23:39

5/10 23:365/11 23:365/12 23:34

Temperature [C] 0

1000

2000

3000

4000

5000

6000

5/6 23:475/7 23:455/8 23:405/9 23:39

5/10 23:365/11 23:365/12 23:34

Qdot to Tank (Watts)

Energy Rate to Tank: Predicted

Energy Rate to Tank: Measured

Fig. 17. Tank temperatures (top), and measured and 

computed clear-day net power to tank (bottom) for SWH #9. Collector Pump Start and Stop Times

110

120

130

140

150

160

170

180

6 8 10 12 14 16 18 20 22

Time of Day (hours)

Julian Day

Start (Measured)

Stop (Measured)

Start (Predicted)

Stop (Predicted)

Fig. 18. Pump start/stop times for SWH #9. 

Data on several of the systems showed unexplained early 

shut-down of the pump. Two examples are shown in Fig. 

19, where shutdown appears early every day. This behavior

is being investigated, possibly related to the collector outlet 

temperature sensor being “glued” to the roof near the 

collector, rather than stably fixed under insulation on the 

outlet manifold.

120

130

140

150

160

6 8 10 12 14 16 18

Time of Day (hours)

Julian Day

Site #4

Site #5

Fig. 19. Pump start/stop times for two SWH in Florida. 

7
4. CONCLUSIONS 

The simple calorimetric method here appears useful for 

diagnosing SWH operations. It works well when the solar 

tank is well-mixed during solar operation, so that the 

measurement at one point on the wall represents the average 

tank temperature. With highly-stratified systems, multiple 

sensors must be used to get a more-accurate measure of 

Ttank,avg. An available spreadsheet tool (6) was developed 

that embodied the analysis method. The method was 

validated with direct solar gain data, with high correlation 

between direct and inferred solar gain (R2

=0.8). 

Malfunctioning systems are easily detected because 

fluctuations in Ttank are very low. Shading and control issues 

with operating systems can be detected. The method could 

be embedded in controllers for continuous diagnostics, and 

could be used in a large-scale program to identify nonperforming systems and gather reliability data.

Future software work includes: i) producing a user-friendly 

version of the software if there is sufficient interest; ii) 

investigating large-scale mail-in projects in collaboration 

with SWH maintenance and repair firms; and iii) potential 

collaboration with controls manufacturers.

5. NOMENCLATURE

A Area

Symbols

cp Heat capacity at constant pressure

C Capacitance of tank (water + walls + instruments)

F Heat removal factor of the collector

K Incidence angle modifier

Q Thermal energy

t Time

T Temperature

U Unit area conductance

α Short-wave absorptivity of the absorber

∆ Difference

ε Effectiveness of the heat exchanger

θ Orientation vector (embodies both tilt and azimuth)

τ Transmission of the glazing(s)

amb Ambient 

Subscripts

avg Average

beg Beginning of a time interval

clr Clear day, assumed for expectation calculations

coll Collector

dot Denotes time derivative of the variable

end End of a time interval

env Environment of the tank

hx Heat exchanger

i Index for data points

IAM Incidence angle modifier

loss Loss from the tank walls

mod Model

n Normal to the collector plane

r Heat removal

tank Solar storage tank

useful Useful energy exiting the collector

6. ACKNOWLEDGMENTS

The authors acknowledge funding from the U.S. Department 

of Energy’s Buildings Technology Program, Innovative 

Systems Program, Solar Heating and Cooling sub-program, 

managed by Bob Hassett. 

7. REFERENCES

1. Burch, Jay, “Homeowner Tests for Diagnosing Solar 

Water Heaters”, 1996 draft report, NREL. Available at 

ftp://ftp.nrel.gov/pub/solar_waterheat-out/tank_calorimetry/

2. ASHRAE 1991, “Installation Manual for Solar Water 

Heating Systems”, Appendix G. Available at 

http://www.solar-rating.org/commercial/guidelines.htm. 

3. Burch, Jay, Xie, Y. and Murley, C., “Field Monitoring 

of Solar Domestic Hot Water Systems Based on Simple 

Tank Temperature Measurement”, Proc. American Solar 

Energy Society Annual Conference, 1995, San 

Francisco, CA

4. Buckles, W. E., "Solar Term Monitoring and 

Performance Evaluation of Solar Domestic Hot Water 

Systems", Masters Thesis, University of Wisconsin, 

Solar Energy Lab, 1983.

5. Barker, Greg, Burch, J., and Hancock, E., “Field tests of 

a Short-term Monitoring Method for Solar Domestic 

Hot Water Systems”, Solar Eng 1990, Miami, FL.

6. The tool SWH diagnostics.xls is available at 

ftp://ftp.nrel.gov/pub/solar_waterheat-out/tank_calorimetry/

7. Burch, J., Shoukas, G, Brandemuhl, M, and Krarti, M., 

“Test and Rate Methods for Thermosiphon solar water 

heaters”, Proc. American Solar Energy Society Annual 

Conference, 2006. 

8. Duffie, John, and Beckman, W., “Solar Engineering of 

Thermal Processes”, 2nd Edition, John Wiley and Sons, 

NY, NY, 1992.

9. Paul Norton, Magnusson, L., Hendron, B., Hancock, E., 

and Barker, G. “Building America Field Test Report: 

NREL/Wonderland Development: Wonderland NearZero Energy Row Houses- Boulder, CO”, available at 

ftp://ftp.nrel.gov/pub/solar_waterheat-out/tank_calorimetry/. 

10. Nathan Aronson, Rubio, M., “Final report on Field 

Monitoring of 10 Drainback Systems”, report to NREL 

from FAFCO, Inc., available from the first author. 

8
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Diagnosis of Solar Water Heaters Using Solar Storage Tank Surface 

Temperature Data

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

Based on the tank energy balance, net gain into the solar storage tank can be inferred from the time derivative of the 

average tank temperature. Using temperature at the tank wall under insulation as a surrogate for fluid temperature, 

sensor mounting is simple and the inferred gains are useful for solar water heater (SWH) diagnostics. Positive 

daytime gain is compared to solar gain computed from site-specific parameters for an assumed clear day. The solar 

storage tank loss coefficient is inferred from temperature decay at night and is compared to the value computed from 

tank description. Larger draws are evident as sharp drops in storage temperature. Analyses are embodied in a tool 

validated against directly measured gains. Inoperative single-family SWHs are easily detected, signaling the need for 

repair. Detection of system control issues and shading are exemplified. In large multi-family systems, frequent daytime draws will bias the comparison to expected solar gain.

15. SUBJECT TERMS

PV; solar water heater; storage tank; diagnostics; temperature data; solar gain; loss coefficient; 

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