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

An Analysis Model for Domestic 

Hot Water Distribution Systems 

Preprint

Jeff Maguire and Moncef Krarti

University of Colorado at Boulder

Xia Fang

National Renewable Energy Laboratory

Presented at the 5th International Conference on Energy 

Sustainability and Fuel Cells

Washington, D. C.

August 7-10, 2011

Conference Paper

NREL/CP-5500-51674

November 2011
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1 

AN ANALYSIS MODEL FOR DOMESTIC HOT WATER DISTRIBUTION SYSTEMS

Jeff Maguire

University of Colorado

Boulder, Colorado, USA

Xia Fang, PE, CEM, LEED® AP

National Renewable Energy 

Laboratory

Golden, Colorado, USA

Moncef Krarti, PhD, PE, LEED® AP

University of Colorado

Boulder, Colorado, USA

ABSTRACT

A thermal model was developed to estimate the energy 

losses from prototypical domestic hot water (DHW) 

distribution systems for homes. The developed model, using the 

TRNSYS simulation software, allows researchers and designers 

to better evaluate the performance of hot water distribution 

systems in homes. Modeling results were compared with past 

experimental study results and showed good agreement. The 

model was also compared with existing domestic hot water 

distribution system modeling software HWSIM for verification. 

The developed model has several capabilities that are not 

available in HWSIM, including the ability to integrate any new 

or existing types of water heater systems, the ability to handle 

several simultaneous draws to different end uses, and the ability 

to handle unique annual draw profiles instead of weekly draw 

profiles. It also allows for draw profiles and ambient conditions 

to be considered using any time resolution. To demonstrate the 

abilities of this new model, a series of sensitivity analyses were 

performed using a benchmark domestic hot water distribution 

system. The effects of adding insulation to the domestic hot 

water distribution system of homes with a gas water heater and 

a solar water heater were also examined. 

NOMENCLATURE

cp: Specific Heat of Water

DHW: Domestic Hot Water

EF: Energy Factor

gpm: Gallons per Minute

lpm: Liters per Minute

PEX: Cross-linked Polyethylene

Q: Heat Loss

Rc,i: Inner Convection Thermal Resistance

Rc,o: Outer Convection Thermal Resistance

Rcond: Conduction Thermal Resistance

Rnet: Net Thermal Resistance

Rrad: Radiation Thermal Resistance

SF: Solar Fraction

t: Time

T: Temperature

Ta: Ambient Temperature

Tf: Final Temperature

Ti: Initial Temperature

Tp: Pipe Temperature

UA: Overall Heat Transfer Coefficient

ε: Emissivity

INTRODUCTION

In any residence with hot water service, thermal losses 

occur in the pipes that connect the water heater to end use

fixtures in the home. The magnitude of these losses depends on 

the location and layout of the distribution system, the 

homeowner’s hot water use, and other factors. Thus, detailed 

models of domestic hot water (DHW) distribution systems are 

needed to make accurate predictions of the distribution losses. 

Several models of residential DHW distribution systems

are available. The National Association of Home Builders 

developed a plug flow model of a DHW distribution system and 

ran it for several cases [1]. However, this model was based on

theoretically derived heat transfer coefficients for losses

between distribution pipes and ambient air and was not 

compared to experimental data. Researchers at Oak Ridge 

National Laboratory also developed a DHW distribution system 

model and used it to evaluate several distribution system 

layouts [2]. However, they used draw profiles that represented 

the best and worst case scenarios of a highly clustered set of 

draws and only “cold start” draws where the pipe always starts 

at ambient temperature, respectively. This provides the extreme 

bounds for losses without necessarily estimating the actual 

losses. Their work also focused exclusively on homes in 

California. A third model was developed by Florida Solar 

Energy Center for use in whole home building energy 

simulation software [3]. This model has undergone only limited 

validation and cannot simulate complex distribution systems. 

Based on a review of the existing models, the HWSIM 

program is the most detailed DHW distribution system 

modeling tool available. It was originally developed in 1990 for 
2 

use in developing the California Title 24 Residential Building 

Standards [4]. The original model had several simplifications 

and could not accurately model thermal losses through a 

distribution system under typical operating conditions. The 

original HWSIM program has since been updated and can now 

model a full week of unique draws, several types of pipes, 

ambient temperatures that vary hourly and mains temperatures 

that vary monthly. The heat loss through pipes was validated by 

comparing the results to a study performed by Applied Energy 

Technology that specifically examined the heat loss in DHW 

distribution system pipes [5]. However, HWSIM cannot model 

multiple draws simultaneously and can use only one week of 

unique draws. Moreover, it is a standalone program that cannot 

be integrated with whole home energy models and features a 

limited number of water heaters. This modeling tool has been 

used to analyze the cost effectiveness of specific energy saving 

measures that could be used in DHW distribution systems for 

homes using a gas storage water heater [6]. 

Of all the discussed existing DHW distribution system 

models, only HWSIM is publicly available. The model 

developed here is also an in house model at the current stage 

due to the lack of a simple user interface and the complexity of 

changing distribution system layouts to model alternate 

distribution systems. 

The goal of this paper is to present a new thermal model 

for a prototypical DHW distribution system that could easily be 

used with unique water heater models or whole house energy 

models. It was implemented in the TRNSYS environment [7], 

which can simulate entire buildings and is often used to model 

the performance of water heaters [8,9]. This model was verified 

against measured data and predictions from the HWSIM model 

using a prototypical distribution system. Parametric cases were 

then modeled with different pipe insulation, different climates 

and low, medium, and high hot water draw profiles. The 

distribution losses were also modeled for a gas and a solar 

water heater. 

MODEL DESCRIPTION 

 The developed model uses a simplified “plug flow” model 

of fluid flow in pipes [7] that breaks the pipes into small 

discrete sections. Each has its own temperature and the size of 

each section is determined by the flow rate in the pipes and the 

time step size. This model neglects axial conduction in the pipe 

and any mixing between sections. The thermal mass of the 

pipes is also neglected in this model, although a modification 

was made to the overall heat transfer coefficient to account for 

this effect between draws when the water is still and cools to 

the ambient temperature. To minimize the size of individual 

sections and model realistic hot water use, a small time step 

size (6 seconds) was chosen for all simulations. The heat loss 

from each section to the environment while water is flowing 

through the pipes is determined by calculating the heat transfer 

coefficients for the pipe inner and outer surfaces and the 

thermal resistance of the pipe and any insulation as shown in 

Equations 1-2. 

ܳ ൌ ܷܣ൫ܶ௣ െ ܶ௔൯ (1) 

ܴ௡௘௧ ൌ ଵ

௎஺

ൌ ܴ௖,௜ ൅ ܴ௖௢௡ௗ ൅ ோ೎,೚ோೝೌ೏

ோ೎,೚ାோೝೌ೏

 (2) 

The overall heat transfer coefficient for pipes in the 

distribution system is determined analytically using a simple 

thermal resistance network [10]. For the inner heat transfer 

coefficient, an exact solution is used for laminar flow and the 

Dittus-Boelter correlation for turbulent flow [10]. For the outer 

heat transfer coefficient, radiation and natural convection are 

considered in parallel. The radiation heat transfer is calculated 

based on an emissivity (ε) of 0.9 for any insulating material and 

0.6 for copper pipes. The emissivity of copper can vary 

significantly depending on the surface finish of the pipes and 

was not measured during testing. For this model, the emissivity 

of copper was determined by comparing the analytic model to 

test results and adjusting the emissivity to obtain good 

agreement (Table 1). For natural convection, a correlation 

developed by Churchill and Chu for horizontal pipes, valid 

under a wide range of Rayleigh numbers, was used [11]. 

Testing showed only a slight difference between heat losses for 

horizontal and vertical pipes [5], so this correlation was applied 

to pipes in both horizontal and vertical orientations. During 

periods of no flow when water in the pipes is still, the pipes and 

the water were assumed to be at equilibrium so the same 

temperature was applied to both and a lumped parameter model 

was used. The pipe temperature was calculated according to 

Equation 3. 

்ି்೑

்೔ି்೑

ൌ exp ൬െ ௎஺௧

௠௖೛

൰ (3) 

To validate this assumption, the Biot number, defined as 

the ratio of conduction resistance inside the body to the thermal 

resistance at the surface of the body, was calculated. Cases with 

a Biot number less than 0.1 are generally considered to have a 

very small error associated with the lumped parameter 

assumption. For the worst case of uninsulated 3⁄4 in. (19.05 mm) 

diameter pipes at 120°F (49°C) in air at 68°F (20°C), the Biot 

number was 0.106. Lower pipe temperatures, insulation, and 1⁄2 

in. (38.1 mm) diameter pipe all reduce the Biot number below 

TABLE 1: IMPACT OF COPPER EMISSIVITY ON 

OVERALL HEAT TRANSFER COEFFICIENT 

ε

Root Mean Square Error 

Between Measured and 

Calculated UA 

Average Error 

Between Measured 

and Calculated UA 

0.5 0.146 5.77% 

0.6 0.101 4.57% 

0.7 0.167 7.69% 
3 

0.1. The thermal mass of the insulation was neglected because 

it is very small compared to the thermal mass of the water and 

pipe. Heat loss through fittings is also neglected because the 

surface area of all the fittings in the distribution system was 

calculated to be less than 1% of the pipes’ surface area. 

For each distribution system, a full year was modeled. This 

allows realistic annual draw profiles to be simulated.

BENCHMARK DISTRIBUTION SYSTEM

The prototypical distribution system modeled is based on 

the Building America program benchmark home [12], which

reflects typical construction during the mid-1990s and serves as 

a baseline for model comparisons. The prototypical distribution 

system used in the benchmark home is based on a study of 

California homes that was performed to determine typical 

distribution system layouts for several homes of different sizes 

[13]. The layout considered in this analysis is designed for a 

one-story, slab-on-grade, 2010-ft2 (187-m2

), three-bedroom 

two-bathroom home (see Figure 1). The distribution system is a 

trunk-and-branch configuration (see Figure 2) consisting of 

uninsulated copper piping where the water heater and the first 

10 ft (3.05 m) of pipe are in an unconditioned garage. The 

Building America Benchmark calls for the water heater to be 

located in conditioned space, so scenarios of locating the water 

heater and part of the distribution system in both conditioned 

and unconditioned spaces were modeled.

While only one distribution system is considered in the 

analysis, many distribution system layouts are possible. 

Distribution system layouts vary significantly depending on the 

floor plan of the home and where the water heater. Additionally, 

in each home different distribution system layouts (such as a 

home run system), whether the distribution system is located in 

conditioned or unconditioned space, and the inclusion of a 

recirculation loop will change the energy use when compared to 

a trunk and branch configuration. While this layout used here is 

considered to be prototypical, the aforementioned factors mean 

the results of this study cannot be extrapolated to all homes. 

Occupant behavior has a significant impact on the overall 

losses in a DHW distribution system: clustered events have less 

heat loss than spread-out events as the pipes have less time to 

cool to the ambient temperature. To capture the effects of 

occupant behavior on the distribution system, the Domestic Hot 

Water Event Schedule Generator was used [14]. This tool uses 

past surveys of homes to determine the probability of hot water 

events associated with various end uses (sinks, showers, baths, 

and appliances), then generates a full year of discrete events for

each fixture based on the probability distribution (see Figure 3). 

Figure 4 shows the distribution of the duration of the hot water 

FIGURE 1: DISTRIBUTION SYSTEM OF THE 

BENCHMARK HOME

FIGURE 2: ISOMETRIC DRAWING OF THE 

DISTRIBUTION SYSTEM

FIGURE 3: A SAMPLE DAY DRAW PROFILE AND 

ASSOCIATED PROBABILITY DISTRIBUTION
4 

events, with the highest frequency of events having short 

durations and lower frequency as the duration increases. Figure

5 shows the distribution of event flow rates for all events, with 

an average flow rate of about 1 gpm (3.78 lpm). While Figure 5 

shows the distribution of flow rates for all events, each end use 

has its own probability distribution.

Another feature of the DHW Event Schedule Generator is 

that the events are more realistic if a shorter minimum duration 

is specified. As an example, consider an occupant using a sink 

for 10 seconds. If a minimum duration of 1 minute is specified, 

the volume drawn during this 10 second event will be spread

out over 1 minute, resulting in a flow rate that is one sixth the 

actual event flow rate. This has two impacts on the calculation 

of distribution losses: the lower flow rate yields a lower heat 

transfer coefficient calculated for the inner surface of the pipe

and the flow is modeled to have ended 50 seconds later than the 

actual event. This results in higher temperatures of the pipe and 

its entrained water at the end of the 1 minute draw than it would 

be for the ten second event. For this study, draw profiles with a 

6 second minimum duration were used to capture actual 

occupant sink use. These are small enough to capture realistic 

hot water use, especially during sink draws, while having a 

reasonable run time in the distribution system model. It was 

also found that 6 second draw profiles show good agreement 

FIGURE 6: COMPARISON OF THE DISTRIBUTION 

LOSSES TO A ONE SECOND HIGH USE DRAW 

PROFILE

TABLE 2: DRAW VOLUMES FOR DIFFERENT SIX 

SECOND DRAW PROFILES IN GAL/DAY (L/DAY)

 Low Use Medium 

Use High Use

Bath 1 2.92 (11.1) 3.76 (14.2) 4.67 (17.7)

Bath 2 1.44 (5.45) 1.23 (4.66) 0.98 (3.71)

Clothes 

Washer 12.3 (46.6) 15.1 (57.2) 16.7 (63.2)

Dishwasher 4.12 (15.6) 4.98 (18.9) 5.71 (21.6)

Kitchen 

Sink 11.3 (42.8) 13.2 (50.5) 15.8 (59.8)

Sink 2 1.63 (6.17) 1.74 (6.59) 2.18 (8.25)

Sink 3 1.53 (5.79) 2.11 (7.99) 2.14 (8.10)

Sink 4 1.61 (6.09) 2.14 (8.10) 2.41 (9.12)

Shower 1 14.4 (54.5) 15.1 (57.2) 18.2 (68.9)

Shower 2 3.71 (14.0) 5.56 (21.0) 6.19 (23.4)

TOTAL 55.0 (208) 64.9 (246) 75.0 (284)

FIGURE 5: HISTOGRAM OF EVENT FLOW RATE 

FOR ALL DRAW PROFILES

FIGURE 4: HISTOGRAM OF EVENT DURATION FOR 

ALL DRAW PROFILES
5 

with 1 second draw profiles in terms of the distribution losses 

(see Figure 6).

This model also takes into account some event clustering 

for each water end use, as well as differences in weekday use 

vs. weekend use. It also includes two separate 1 week vacation 

periods. Any time step size can be used and multiple events 

going to different end uses can occur simultaneously. Three 

draw profiles representing low, medium, and high use 

households (Table 2) were modeled to determine the impact of 

DHW use on the thermal distribution losses.

Two types of hot water draws were considered for this 

model. For sinks, showers, and baths, most occupants will wait 

until a minimum “useful” temperature is reached before 

actually using any hot water. To model this, any water drawn 

below a minimum useful temperature of 105°F (40.6°C) was 

considered to be wasted. This wasted water is also a 

distribution system loss because the energy spent on heating the

water from mains temperature to the water heater set point is 

completely lost during this “warm-up” period. For appliances

(dishwashers and clothes washers), any temperature of water 

drawn was considered useful. A third potential use option, a so 

called “Btu draw” where the final temperature of all the water 

drawn must be above the useful temperature is possible for bath 

draws depending on occupant behavior, but was not modeled in 

this study. When analyzing the results, useful and wasted hot 

water draws are disaggregated and the energy associated with 

the wasted hot water is included in the overall distribution 

system losses.

VALIDATION ANALYSIS

The predictions of the TRNSYS model were compared 

against measured data. Past work had measured the heat loss 

for copper and PEX-AL-PEX piping (PEX-AL-PEX consists of 

a thin layer of aluminum sandwiched between two layers of 

PEX) [5], with and without insulation, under typical DHW 

distribution system conditions. Tests were performed on long 

segments (over 80 ft or 24.4 m) of 1⁄2 in. (12.7 mm) and 3⁄4 in. 

(19.05 mm) pipe under controlled laboratory conditions for 

flow rates between 0 and 5 gpm (0 to 18.9 lpm). The overall 

heat transfer coefficient was calculated while hot water was 

being delivered and while the pipes cooled between draws. For 

the insulated pipes, tests were run with 1⁄2 in. (12.7 mm) and 3⁄4

in. (19.05 mm) thick insulation for both 1⁄2 in. (12.7 mm) and 3⁄4

in. (19.05 mm) diameter pipes using insulation with a rated 

R/in. value of 3.97 ft2

·hr·°F/Btu·in. (0.275 m2·K/W·cm). Some 

unique flow phenomena, such as slip flow and stratified flow, 

were observed during this testing for flow rates in the 

transitional flow regime. These phenomena are not accounted 

for in the developed model. 

The results of comparing the model predictions against 

measured data are presented in Figures 7, 8, and 9. In general, 

the model more closely matches the test data at higher flow 

rates. At any flow rate, insulation causes a larger discrepancy 

than for the uninsulated case. This discrepancy increases as the 

thickness of the insulation increases. The larger discrepancies 

between the model and measured data at lower flow rates for 

any level of insulation occur because of the unique flow 

phenomena, which would be very difficult to capture in a 

model. The discrepancies caused by the addition of insulation 

FIGURE 7: TIME FOR COPPER PIPES TO COOL 

FROM 135°F (57.2 °C) TO 105 °F (40.6 °C) IN 67.5°F 

AMBIENT AIR

FIGURE 8: TEMPERATURE DROP IN 100 FT (30.5 m) 

OF 1⁄2 in (12.7 mm) DIAMETER COPPER PIPE WITH A 

135°F (57.2°C) INLET TEMPERATURE AND 67.5°F 

(19.7°C) AMBIENT AIR

FIGURE 9: TEMPERATURE DROP IN 100 FT (30.5 m) 

OF 3⁄4 in. (19.05 mm) DIAMETER COPPER PIPE WITH A 

135°F (57.2°C) INLET TEMPERATURE AND 67.5°F 

(19.7°C) AMBIENT AIR
6 

could be related to nonuniform properties or underperformance

of the insulation. Additionally, it is assumed that the 

surrounding air was still during testing and modeling, but 

airflow around the insulation during testing could cause a 

discrepancy between the measured data and the model 

predictions.

A comparative analysis was also performed to compare the 

performance of the model to the DHW distribution system 

modeling tool HWSIM. For this analysis, the benchmark 

distribution system was modeled using the HWSIM framework 

with a realistic week of draw profiles. However, the TRNSYS 

model and the HWSIM tool treat draw profiles somewhat

differently: draw profiles in the TRNSYS model are assumed to 

include hot water waste for mixed events and those in the 

HWSIM tool do not. The TRNSYS draw profiles are therefore 

specified as the draw by the water heater, while the HWSIM 

draw profiles are specified as the draw of hot water at the end 

use. This complicates a direct comparison. In an attempt to 

compensate for this difference, additional distribution system 

losses and hot water use were added to the TRNSYS model 

results during post-processing based on the losses observed 

during the useful portion of minimum temperature events. This 

made the comparison more direct but led to an overprediction 

of the losses and hot water use. The results of this comparison 

and the difference between the HWSIM prediction and the 

postprocessed TRNSYS model are shown in Table 3. All other 

draw profiles used for modeling in TRNSYS included hot water 

waste, so no postprocessing of this nature was required for any 

other simulations. To minimize the differences between the two 

models on simulations of a full year of draw profiles (HWSIM 

models one week for each month of the year while the 

TRNSYS model considers every day of a full year), the 

ambient and mains temperature for both models were kept 

constant year round. 

BENCHMARK DISTRIBUTION SYSTEM RESULTS

To examine the distribution losses in U.S. homes, the 

benchmark distribution system was modeled for homes located 

in various US climate zones. In particular, the benchmark home 

was simulated in five of the climate zones referenced in the 

Building America Program [15], which were chosen because 

they encompass the majority of the U.S. population. The five 

selected locations represent the cold (Chicago, Illinois), mixedhumid (Atlanta, Georgia), hot-humid (Houston, Texas), hot-dry 

(Phoenix, Arizona), and marine (Seattle, Washington) climates. 

Benchmark homes with the same floor plan as used for the 

prototypical distribution system were first simulated in a 

separate building modeling program, BEopt [16]. BEopt

computed hourly indoor ambient air temperature, 

unconditioned garage temperature, and mains temperature, 

which were used as inputs to the TRNSYS model. Modeling 

the homes separately from the distribution system substantially 

reduces the simulation run time required to analyze the 

distribution system, although it would be possible to also model 

the homes in the TRNSYS environment with the distribution 

system to capture the interactive effects of the distribution 

system with the building thermal load (in warm climates, the 

distribution system would likely lead to an additional cooling 

load, while in cold climates it would lead to a reduction in the 

heating load). In all climates, the water heater was modeled as a 

40-gal (151-L), 0.59 energy factor (EF) gas storage water 

heater set to 120°F (48.9°C). A multinode water heater model 

was used to capture any potential stratification in the tank.

Model parameters for this water heater were derived from

the standard rating test results [17]. The hot water use was 

modeled using the previously described DHW event generator 

tool with a minimum event duration of 6 seconds and the draw

characteristics shown in Table 2. 

 The benchmark homes show significant differences in the 

distribution loss for homes in different climates and with 

different DHW usage (Figures 10 and 11). The distribution 

losses in the most extreme case of a high use home in a cold 

climate are twice large as those in a low use home in a hot 

climate. In most climates, the average difference is 30% in the 

TABLE 3: COMPARISON OF DISTRIBUTION LOSSES 

PREDICTED BY THE TRNSYS MODEL AND HWSIM 

TOOL

 HWSIM TRNSYS Difference

Hot Water Use In

Gal/Day (L/Day)

71.1

(269)

73.4

(228) 3.24%

Hot Water Waste in 

Gal/Day (L/Day)

10.1

(38.2)

10.7

(40.5) 5.55%

Distribution System 

Losses in kBtu/Day 

(kWh/day)

6.99 

(2.05)

6.98 

(2.04) 0.11%

FIGURE 10: WATER HEATER ENERGY 

CONSUMPTION FOR THE BENCHMARK HOME 

DISAGGREGATED INTO USEFUL ENERGY AND 

WASTE
7 

distribution losses for each home between the low use and high 

use cases. 

 To determine the impact of potential distribution system 

improvements, a full parametric study was carried out using 

different draw profiles, climate zones, water heater locations, 

and distribution system insulation levels to determine the site 

energy consumption for all cases. Due to the exhaustive nature 

of this parametric analysis, only selected results are presented

here. The energy consumed by the water heater is significantly 

influenced by placing it in unconditioned space, but because 

only the first 10 ft (3.05 m) of the distribution system was 

located in unconditioned space, the difference between the 

distribution thermal losses for a water heater in conditioned 

space vs. unconditioned space was fairly modest: less than 3% 

or 1 therm (29.3 kWh) in the most extreme case. In hot climates 

(Phoenix and Houston), placing this piping in unconditioned 

space reduced the overall distribution system losses by as much 

as 0.5 therm (14.65 kWh) because of the high ambient air 

temperatures in the unconditioned space. Adding insulation to 

the distribution system can reduce the distribution losses by as 

much as 20%, but increasing the insulation thickness from 1⁄2 in. 

(12.7 mm) to 3⁄4 in. (19.05 mm) only slightly further reduces the

distribution losses. This 20% decrease reduces the whole house 

energy consumption by only 7.11 therms (208 kWh) if 1⁄2 in. 

(12.7 mm) insulation is used and by 8.68 therms (254 kWh) if 

3⁄4 in. (19.05 mm) insulation is used in a home in a cold climate, 

assuming none of the distribution losses affect the home’s 

heating load. In reality, the actual reduction in energy 

consumption will likely be less.

In addition to examining insulating the full distribution 

system, the impact of insulating just the portion in 

unconditioned space was examined. This should be the most 

important area to insulate because of the higher temperature

difference between the pipe and the ambient air, especially in 

cold climates. This section also has all draws and the highest 

temperature water running through it. Insulating this section 

provides a large benefit, saving as much as 30% of the energy 

that could be saved from insulating the full distribution system 

for cold climate homes in the case of a low use home (see 

Figure 12). Even for homes in hot climates, at least 20% of the 

savings associated with insulating the distribution system could 

be achieved by insulating this section, which makes up just 8% 

of the distribution system. This is consistent with 

recommendations for commercial buildings [18] that the first 8

ft (2.44 m) of pipe always be insulated for any water heater. 

This recommendation is sometimes made for residential 

buildings [19], but it is not universally recommended [20].

To determine the cost effectiveness of adding insulation, 

the simple payback period was calculated for the benchmark 

homes. Cost estimates for pipe insulation [21] average about 

$1.90/ft ($6.23/m) for 1⁄2 in. (12.7 mm) insulation and $3.30/ft 

($10.80/m) for 3⁄4 in. (19.05 mm) insulation. For the payback 

calculation, the distribution system loss’s impact on home 

heating and cooling load was not counted, so a more detailed 

analysis would be required to fully explore the payback 

associated with adding insulation. To determine the monetary

value of the energy savings the most current statewide average 

for the cost of gas in each location was used (see Table 4) [22]. 

FIGURE 12: IMPACT OF INSULATING ONLY PIPES IN 

UNCONDITIONED SPACE

TABLE 4: ENERGY COSTS AT BENCHMARK HOME 

LOCATIONS IN $/THERM (¢/kWh)

Electricity Gas

Atlanta 2.97 (10.14) 1.48 (5.04)

Chicago 3.57 (12.20) 0.86 (2.93)

Houston 3.41 (11.63) 0.95 (3.26)

Phoenix 3.21 (10.97) 1.55 (5.29)

Seattle 2.42 (8.26) 1.14 (3.91)

FIGURE 11: DISTRIBUTION THERMAL LOSSES FOR 

THE BENCHMARK HOME WITH A WATER HEATER 

LOCATED IN CONDITIONED SPACE
8 

In the most cost effective case of adding 1⁄2 in. (12.7 mm) 

insulation to the distribution system of a high use home in 

Atlanta, the simple payback is estimated to be 26 years. Adding 

insulation to just the section of piping in unconditioned space 

appeared much more cost effective, with simple paybacks less 

than 10 years possible for homes in colder climates.

DISTRIBUTION LOSSES FOR SOLAR WATER 

HEATERS

The distribution losses for a home using a solar water 

heater were also evaluated using the developed model. With a 

solar water heater, the storage tank temperature varies as energy 

is gathered from the collector and stored in the tank, further 

increasing the distribution losses. Two solar water heating 

systems were modeled: a larger system for colder climates 

(Chicago and Seattle), and a smaller system for warmer cities 

(Houston, Phoenix, and Atlanta) as shown in Table 5. For all 

systems, an indirect, forced circulation system using a 50/50 

mix of glycol and water was used and the collector was 

oriented due south with a slope equal to latitude. The piping

between the collector and the storage tank was assumed to be 50 ft (15.24 m) long with 3⁄4-in. (19.05-mm) insulation based on 

solar water heating system certification guidelines [8]. The 

storage tank temperature was set to 120°F (48.9°C) and the 

collector stopped circulating if the tank temperature exceeded 

140°F (60°C) as specified by the manufacturer of this system. 

In both cases, a single tank with R-16 insulation and a backup 

electric heating element with a maximum output of 4.5 kW 

(15,355 Btu/h) was used. The model parameter for the 

insulation of this storage tank was derived from past work on a 

solar water heater with similar insulation [23]. Other model 

parameters, such as heat exchanger size, were derived from 

manufacturer specifications. A multinode tank model was also 

used for this case to capture stratification (which is much more 

important than for a gas storage water heater). The same six 

second high, medium and low draw profiles previously 

presented were used for this analysis. The impact of insulation 

on the distribution system losses was also examined.

The distribution system losses were found to be larger in 

all cases with a solar water heater (see Figure 13). In the most 

extreme case in of a high use home in Seattle, the losses were 

as much as 16% larger. The largest additional loss from the 

distribution system was 6.52 therms (191 kWh) more than the 

benchmark home for a high use home with uninsulated pipes in 

Seattle. The impact of adding insulation to the distribution 

system was similar to what was seen in the case of a gas water 

heater. There was an average 25% difference on the distribution 

losses between high and low use homes across the five climates

(see Figure 14). There were also significant differences between 

the distribution losses, depending on climate. The distribution 

losses in Phoenix are about 35% lower than those in Chicago.

 The impact of insulation on the piping between the solar 

collector and the water heater was also examined (see Figure 

15). Standard practice is to include some insulation on this 

loop. For a solar water heating system to be certified in the 

United States, the collector loop must be insulated by insulation 

TABLE 5: FEATURES OF SOLAR WATER HEATING 

SYSTEM FOR HOT AND COLD CLIMATES

 Cold Climates Warm Climates

Collector Area 50.7 ft2 (4.71 m2

) 39.8 ft2 (3.70 m2

Storage Tank 

Volume

80 gallons 

(303 L)

50 gallons

(189 L)

Rated Solar 

Energy Factor 2.1 1.8

FIGURE 13: COMPARISON OF THE DISTRIBUTION 

SYSTEM LOSSES WITH GAS AND SOLAR WATER 

HEATERS IN CONDITIONED SPACE WITH NO 

INSULATION

FIGURE 14: IMPACT OF INSULATION ON 

DISTRIBUTION SYSTEM LOSSES FOR HOMES 

WITH A SOLAR WATER HEATER IN 

CONDITIONED SPACE
9 

with a minimum R-value of 2.6 ft2

·hr·°F/Btu (0.46 m2·K/W) 

[8]. Not insulating this loop seriously affects the performance 

of the solar water heater. A well-insulated loop can increase the 

solar fraction of the water heater by as much as 0.07, reducing 

the energy consumption of the water heater by 4.8 therms/yr 

(142 kWh/yr). 

An economic analysis was also performed to determine the 

simple payback of adding insulation to the distribution system 

for a home with a solar water heater. In this case, the backup 

heat source is electric, so the most recent state average 

electricity prices were used for each home [24]. While the 

payback period is still always above 18 years, it is generally 

better than for homes using a gas water heater. This is due to 

both the increased temperature of water flowing through the 

distribution system and the higher cost of electricity. This can 

be generalized to other water heating options as well: insulating

the DHW distribution system will be more attractive 

economically if the water heater is set to a higher temperature 

or if fuel costs increase. In this case, adding insulation to the 

DHW distribution system piping is more attractive than for 

homes with a gas water heater.

SUMMARY AND CONCLUSIONS

A modeling tool for analyzing DHW distribution systems 

was developed. The predictions of the model were found to 

reasonably match laboratory data for the losses in distribution 

systems as well as other distribution system models. Some 

discrepancies did occur between lab testing and the model, but 

can be attributed to flow phenomena too complex to properly

model, possible underperformance of pipe insulation, and 

potentially moving air in the lab during testing. The TRNSYS 

model does not have the flexibility of other competing models 

in terms of quickly testing alternate distribution system layouts, 

but it does allow the distribution losses to be determined for a 

variety of water heater types This model also allows a full 

unique year of draws to be examined and uses a previously 

existing probabilistic draw profile generator to model realistic 

occupant behavior.

This model was applied to a benchmark distribution system 

for homes with a standard gas water heater and a solar water 

heater. For both cases the losses of the distribution system were 

examined under several possible sets of conditions that may be 

present in a home in several climates, including distribution 

systems with and without insulation, variable occupant 

behavior in terms of annual draw volume, and locating the 

water heater in conditioned or unconditioned space. The 

distribution losses varied significantly depending on all these 

parameters; the worst-performing system lost more than three 

times the energy as the best.

 The impact of insulation was examined in detail and found 

to have a significant impact. Insulating the first few feet of the 

distribution system, particularly in homes with the water heater 

in unconditioned spaces, had significant energy savings, in 

some cases up to 30% of the savings that could be achieved by 

insulating the entire distribution system. Insulating the collector 

loops in the case of solar water heaters also significantly 

increased the performance of the solar water heater. The simple 

payback was examined for distribution system insulation and 

was found to be rather long for insulating the full distribution 

system, but more reasonable for just insulating the first few feet 

of the distribution system. Further modeling efforts combining 

the distribution losses with a whole home building model so 

that the interaction of the two systems could be fully explored 

would be required to verify the calculated payback periods.

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FIGURE 15: IMPACT OF COLLECTOR LOOP 

INSULATION FOR A MEDIUM USE HOME WITH THE 

WATER HEATER IN CONDITIONED SPACE
10

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