Validation of a Hot Water 

Distribution Model Using 

Laboratory and Field Data

C. Backman and M. Hoeschele

Alliance for Residential Building Innovation

July 2013
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iii

Validation of a Hot Water Distribution Model Using 

Laboratory and Field Data 

Prepared for: 

The National Renewable Energy Laboratory 

On behalf of the U.S. Department of Energy’s Building America Program

Office of Energy Efficiency and Renewable Energy 

15013 Denver West Parkway 

Golden, CO 80401 

NREL Contract No. DE-AC36-08GO28308 

Prepared by: 

C. Backman and M. Hoeschele 

Alliance for Residential Building Innovation

Davis Energy Group, Team Lead 

123 C Street 

Davis, CA 95616 

NREL Technical Monitor: Michael Gestwick

Prepared under Subcontract No. KNDJ-0-40340-03 

July 2013 
iv

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v 

Contents

Contents....................................................................................................................................................... v

List of Figures ............................................................................................................................................ vi

List of Tables .............................................................................................................................................. vi

Definitions .................................................................................................................................................. vii

Executive Summary................................................................................................................................. viii

Acknowledgements ................................................................................................................................... ix

1 Introduction ........................................................................................................................................... 1

1.1 Background and Motivation ................................................................................................1

1.2 Research Questions ..............................................................................................................3

2 Methodology ......................................................................................................................................... 4

2.1 TRNSYS Distribution Model Description ...........................................................................4

2.2 TRNSYS Model Validation .................................................................................................6

2.3 Distribution Model Pipe Heat Loss Validation ....................................................................6

2.4 Whole-House Model Validation Effort ...............................................................................8

2.5 Extending the Validated Model to Other Climates ..............................................................8

2.6 Impact of Load Patterns on Distribution System Performance ...........................................9

3 Results................................................................................................................................................. 11

3.1 Model Validation With Laboratory Pipe Heat Loss Data ..................................................11

3.2 Model Validation with Solar Row Field Data ...................................................................13

3.3 Distribution System Performance in Different Climates ...................................................17

3.4 Summary of Latest Findings on Hot Water Usage and Impact of Gas Tankless Water 

Heaters on Use Patterns .....................................................................................................19

4 Conclusions ........................................................................................................................................ 21

4.1 Next Steps ..........................................................................................................................22

5 References .......................................................................................................................................... 23

Appendix A: TRNSYS Documentation for Type 604, TESS (2011)....................................................... 24

Appendix B: Observed Impact of Gas Tankless Water Heater on Hot Water Use Patterns.............. 34
vi 

List of Figures

Figure 1. Sample distribution system layout ........................................................................................... 5

Figure 2. Sample 6-s interval hot water draw profile for one day .......................................................... 9

Figure 3. Comparison of laboratory and TRNSYS model results for 3⁄8-in. PEX from a cold start ... 13

Figure 4. Wasted water ratio as a function of time between draws (field data).................................. 14

Figure 5. Binned wasted water ratio as a function of time between draws (field data)..................... 15

Figure 6. Comparison of simulated and actual pipe thermal decay over time................................... 16

Figure 7. Comparison of simulated and actual pipe thermal decay with sequential draws ............. 16

Figure 8. Percentage of water heater delivered volume at a temperature > 105°F ............................ 35

Figure 9. Percentage of all hot water draws < 0.5 gallons (pre and post-tankless retrofit) .............. 36

Figure 10. Percentage of all hot water volume < 0.5 gal (pre- and post-tankless retrofit) ................ 37

Figure 11. Percentage of all hot water draws > 5 gal (pre- and post-tankless retrofit)...................... 38

Figure 12. Percentage of all hot water volume > 5 gal (pre- and post-tankless retrofit) ................... 38

Figure 13. Average shower draw duration (pre- and post-tankless retrofit)....................................... 40

Unless otherwise noted, all figures were created by the Alliance for Residential Building 

Innovation team.

List of Tables

Table 1. Comparison of TRNSYS Type 604 and HWSIM Model Capabilities ........................................ 4

Table 2. TRNSYS Model Fixture Description............................................................................................ 5

Table 3. Pipe and Insulation Material Properties ..................................................................................... 7

Table 4. Summary of AET Configurations and TRNSYS Inputs for Model Validation ......................... 7

Table 5. Summary of Laboratory and Simulated Results ..................................................................... 11

Table 6. Comparison of Observed and Modeled Pipe Heat Losses .................................................... 17

Table 7. Modeled Results by Climate Zone ............................................................................................ 18

Table 8. Modeled Climate Zone Insulation Savings Results ................................................................ 19

Table 9. Summary of Hot Water Database Median Usage Characteristics ......................................... 20

Table 10. Monitored Pre- and Post-Retrofit Daily Recovery Loads by Site ........................................ 39

Unless otherwise noted, all tables were created by the Alliance for Residential Building 

Innovation team.
vii 

Definitions

AET Applied Energy Technology

BEopt Building Energy Optimization model

CPVC Chlorinated polyvinyl chloride

DOE U.S. Department of Energy

gpd Gallons per day

gpm Gallons per minute

LBNL Lawrence Berkeley National Laboratory

MRT Mean radiant temperature

NREL National Renewable Energy Laboratory

PEX Cross-linked polyethylene

TRNSYS TRaNsient System Simulation program
viii

Executive Summary

Characterizing the performance of hot water distribution systems is a critical step in developing 

best practice guidelines for the design and installation of high performance domestic hot water 

systems. Overall hot water system performance is dependent upon the complex interactions 

between the household occupants’ behavior, plumbing configuration, climate, and water heater 

characteristics. Historically, building energy simulation models have simplified distribution 

system algorithms to accommodate the longer time steps used in most building performance 

models. The advent of more advanced distribution modeling tools, and ongoing improvements in 

the quality of input data driving these models, lead to better predictive capabilities.

This project validated the TRNSYS Type 604 pipe model against laboratory pipe heat loss test 

data and detailed field monitoring data from a prior National Renewable Energy Laboratory 

(NREL) research project. The Type 604 model is an advanced pipe model that accounts for pipe 

heat transfer effects (convective, radiant, and axial conduction) and pipe heat capacitance effects 

utilizing a nodal pipe network. The model provided very good comparisons with the two 

datasets. Using measured data and an as-built distribution model of the NREL Solar Row project, 

modeled hot water distribution losses were determined to be within 2% of the monitored data 

over the 4-month period when field data were available. Extending the validated model to a 

prototypical distribution system configuration in different climates suggests that for an electric 

storage water heater, distribution losses represent about 26%–27% of the annual water heater 

recovery load, with an associated energy use impact of 534–892 kWh/yr. Insulating the full 

distribution system with 3⁄4 in. pipe insulation reduces the distribution loss by a projected 111–

170 kWh/yr. Different usage quantities and patterns would affect these savings estimates.

Key lessons learned in this work suggest that the model is highly accurate, yet it is complex to 

configure and requires a short time step (on the order of 5–10 s) to accurately model real hot 

water draw events and the cooldown effects between draws. The short time step results in long 

simulation runtimes. This poses an issue for how best to integrate with mainstream building 

energy simulation models that typically operate on a 1-h time scale. While the work 

demonstrated the validity of the model’s underlying capabilities, a remaining key question 

centers on how best to extend this work to the broader energy efficiency community. 

Developing the inputs for the modeling and reviewing current research findings on typical hot 

water usage quantity and patterns highlight the need for better input data on usage and usage 

patterns that are sufficiently granular to assess regional variations in hot water distribution 

installation practices, seasonal variations in load for different climates, and usage variations 

associated with vintages of homes (running the gamut from new highly water-efficient homes, to 

old homes that have had no improvements in appliance and fixture water use efficiency). 

The findings of this work are directed toward researchers focused on understanding distribution 

system performance and overall hot water system performance. Future efforts in this research 

area include collecting better input data to drive the models, completing a parametric study to 

evaluate distribution performance under varying conditions, and determining the best strategy in 

accurately modeling performance without adding excessive complexity and extending simulation 

runtimes.
ix

Acknowledgements 

We would like to acknowledge the support and contributions of Jeff Maguire (NREL), Greg 

Barker (Mountain Energy Partnership), Jim Lutz (Lawrence Berkeley National Laboratory), Jeff 

Thornton (Thermal Energy Simulation Specialists), and Carl Hiller (Applied Energy 

Technology). This project builds upon their prior efforts in both collecting valuable laboratory

and field data and developing and demonstrating the available simulation models. 
1 

1 Introduction

1.1 Background and Motivation

The performance of residential hot water distribution systems is not well understood for several 

reasons, including:

• A lack of validated simulation tools to model system performance 

• Limited field monitoring data that accurately represent the detailed, high-resolution hot 

water usage data needed to drive the models

• Proper characterization of hot water distribution layouts regionally and by house vintage 

• The impact of occupant behaviors in regards to patterns and preferences.

All of these factors contribute to a generally poor understanding of hot water distribution system 

performance, since all of these factors strongly impact overall performance. Limited hard data 

currently exist in quantifying distribution losses, especially in terms of understanding how 

climate, plumbing practices (i.e., pipe layout and variations with house vintage and house 

design), hot water usage patterns, and user behavior affect overall losses. A variety of factors 

contribute to this poor understanding of distribution system performance, including the 

following: 

• Detailed monitoring of water heater energy and water flows to use points is challenging 

and expensive, since remote temperature sensors and individual flow meters are ideally 

required to properly characterize performance. Few studies have been completed that 

accurately quantify single-family home distribution performance, limiting the data 

available for model validation efforts. 

• Detailed water heating distribution system modeling tools have historically been limited 

in their modeling rigor. This, coupled with the uncertainty regarding key inputs, has left 

this area not well understood. 

• For many homes and apartments, much of the distribution piping is largely hidden from 

view, making accurate descriptions difficult of how the pipe is routed and what the actual 

pipe lengths and diameters are. Without knowledge of the pipe layout and end use point 

locations, it is challenging to understand the system interactions. 

• Hot water loads, usage patterns, and user behavior vary widely between similar sized 

households, as well as within a household on a day to day basis. These patterns can 

significantly affect distribution system performance in terms of heat loss and water waste. 

Addressing all of these issues is critical in developing a better understanding of 

distribution system performance. 

Conventional building energy simulation models are typically run on hourly time steps to 

provide an appropriate balance between simulation time and modeling accuracy. An hourly time 

step does not provide sufficient resolution to capture the transient effects associated with hot 

water draw events, which are typically on the order of a few minutes in length (Lutz and Melody 

2012). A robust simulation tool needs to accurately model the physical of the flow and heat 
2 

transfer effects, simulate on a short time step to capture individual hot water events, and be 

driven by accurate input data defining all the key inputs (such as layout, use pattern, etc.). 

The ongoing development of advanced simulation models, such as the HWSIM distribution 

system model (Springer et al. 2008) and recent TRNSYS modeling work completed by the 

University of Colorado at Boulder and NREL (Maguire et al. 2011), represent valuable steps in 

the process of developing the tools to improve quantification of hot water system performance, 

ultimately leading to the identification of cost-effective improvement options that will contribute 

to Building America’s goal of 30%–50% energy savings.

Work is proceeding in many of the identified water heating gap areas, but additional effort is 

needed to generate higher quality input data and improve the simulation tools required to 

properly assess distribution system performance. Current activities include:

• Jim Lutz of Lawrence Berkeley National Laboratory (LBNL) has been working for 

several years on developing a database for archiving high resolution hot water use 

data from field monitoring studies.1 As more studies expand the database, the 

characterization of hot water usage nationally and regionally will improve. 

• LBNL is also working on developing and demonstrating wireless temperature and 

flow measurement systems that will greatly enhance the ability to disaggregate hot 

water use and flow by fixture. LBNL is hoping to demonstrate this system in 

February 2013 and expand to a broader pilot project in the spring of 2013. 

• The Advanced Residential Integrated Energy Solutions Building America team is 

starting a field effort to assess the impact of water heater and distribution system 

upgrades at three or more existing homes in central New York. 

• Davis Energy Group has recently completed simulation enhancements to HWSIM 

(with a focus on integrating both gas storage and gas tankless water heater models 

with the distribution model), as part of a California Energy Commission Public 

Interest Energy Research sponsored advanced water heating project led by the Gas 

Technology Institute. LBNL has a follow-on Public Interest Energy Research project 

that will be completed in 2013 that develops a more comprehensive model that 

incorporates distribution system modeling with a wider range of water heater types. 

• Davis Energy Group also has completed field documentation of installed distribution 

piping in 100 new California homes. Further similar studies of typical plumbing 

practice are needed to document regional variations in distribution system 

configuration. 

The emergence of new water heaters over the past 5–10 years also could impact distribution 

system performance by affecting energy waste and water waste due to variations in outlet water 

temperature and system delivery characteristics. Although storage water heaters have historically 

claimed close to 100% of the U.S. market, the recent inroads of gas tankless water heaters have 

 

1 www.aceee.org/files/pdf/conferences/hwf/2010/3D_Jim_Lutz.pdf
3 

been significant, with recent sales estimates at approximately 400,000 units/yr.2 Tankless water 

heaters operate very differently than storage water heaters, since they heat water only when there 

is a hot water demand. Therefore, a “cold start” situation results in first cold, and then tepid 

water being delivered. This impacts hot water waiting times, and the associated water and energy 

waste while waiting for hot water to arrive at the use point. 

1.2 Research Questions

The primary objective of this project was to build on prior work that ultimately leads to 

developing a better fundamental understanding of residential water heating system performance.

Realizing that there are numerous modeling and data collection efforts underway that will 

ultimately feed into this goal, we see the current effort as a step in the process toward the 

development of the required tools needed for a comprehensive hot water design guide. 

The primary research questions to be answered in this project are: 

How well can detailed distribution system models predict hot water delivery temperatures 

and distribution losses given available input validation data? 

What model limitations are identified as part of this validation effort?

Secondary questions related to the use of these modeling tools include: 

What new information have we learned on hot water usage quantity and patterns (e.g., 

climate impact, seasonal impacts), how do these revised assumptions affect advanced 

system savings projections, and what information is useful in improving modeling 

algorithms (BEopt, HWSIM, TRNSYS, etc.) and supporting standards activities (ASHRAE 

Standard Project Committee 118.2 and the U.S. Department of Energy’s (DOE) Energy 

Factor test procedure update)?

What conclusions can we draw on customer behavioral hot water use (seven sites) as they 

change from conventional storage water heaters to gas tankless water heaters and other 

advanced system options (to the extent data are available)? 

The latter two research questions are peripheral to the primary research questions, yet are tightly 

linked to the goal of better understanding overall system performance, since hot water usage 

quantity and use patterns are critical factors affecting residential hot water system performance. 

 

2 www.energystar.gov/ia/partners/prod_development/new_specs/downloads/ 

water_heaters/Water_Heater_Market_Profile_2010.pdf 
4 

2 Methodology

2.1 TRNSYS Distribution Model Description

The TRNSYS 17 (TESS 2011) simulation program was used in this study to model distribution 

system performance despite Davis Energy Group’s extensive development work over the years 

with the HWSIM simulation tool. Both models offer advantages and limitations, as summarized 

in Table 1. Given that TRNSYS is widely recognized as a mainstream building system 

simulation tool, the decision was made to focus the validation effort on using TRNSYS with the 

newly developed Type 604 “bi-directional node pipe model.” This pipe model, described in full 

detail in Appendix A, is an enhanced version of the Type 709 model used in prior work (Maguire 

et al. 2011). The Type 604 dynamically models pipe exterior surface convective and radiant heat 

transfer effects based on fluid, pipe, and environmental properties, as opposed to Type 709’s use 

of a fixed pipe outside heat transfer coefficient. In addition, the Type 709 model does not 

account for pipe heat capacitance effects, which influence the ability to accurately model hot 

water flows from a “cold start” condition and cooldown heat transfer effects. 

Table 1. Comparison of TRNSYS Type 604 and HWSIM Model Capabilities

TRNSYS Type 604 HWSIM

User-Specified Time Step Yes Yes

Able To Model Simultaneous 

Hot Water Draw Events Yes No overlapping draws 

allowed

Specification of 

Hot Water Loads 

User-specified hot water 

thermal load (i.e., recovery 

load = use + distribution)

End use loads specified 

(distribution system impact 

determines recovery load)

Able To Model 365 

Unique Days of Hot Water 

Draw Events

Yes

No, can model 

7 days/month or 

1 day/season)

Public Domain No Yes

Distinguish Between Volume 

Draws and Draws Requiring 

Minimum Temperature

Yes Yes

Model Hourly Variations in 

Pipe Heat Loss Environments Yes Yes

The Type 604 model is configured for a user-defined piping layout (an example plumbing layout 

is shown in Figure 1) and is driven by a profile of hot water draws imposed on the water heater.

The draw profile is included in the user-defined input file that specifies the end use fixture, draw 

start time, and flow rate. The configuration of the distribution system provides for “valves” at 

each branch point in the distribution system that serve to throttle hot water flows to each branch, 

as needed, under simultaneous hot water draw conditions. Each branch path ultimately ends up 

serving an individual hot water use point. For the plumbing layout shown in Figure 1, Table 2

summarizes the hot water end use points. 
5 

Figure 1. Sample distribution system layout 

Table 2. TRNSYS Model Fixture Description 

Use Point 

Identifier*

Fixture 

Location

1 Clothes Washer

2 Dishwasher

3 Kitchen Sink #1 

4 Sink #2 (Master Bath)

5 Shower #1 (Master Bath)

6 Bathtub #1 (Master Bath)

7 Sink #3 (Master Bath)

8 Sink #4 (2nd Bath)

9 Shower #2 (2nd Bath)

10 Bathtub #2 (2nd Bath)

* As noted in Figure 1
6 

In contrast to TRNSYS, HWSIM (Springer et al. 2008) first defines what the user wants at a 

fixture, including whether a minimum use temperature is needed before the use begins (e.g., 

shower), what the desired use temperature is, the volume of (mixed) water desired, and the flow 

rate. The sizing, configuration, and thermal characteristics of the distribution system impact how 

much water in the hot line needs to be “wasted” until satisfactory hot water arrives.

3

 

TRNSYS reads the draw input file at each time step to drive the hot water demand. The cold 

water inlet temperature is included in the Typical Meteorological Year 3 weather file. If pipes are 

located in unconditioned space, such as the crawlspace, the environment temperature to which 

pipe heat loss occurs was derived from the hourly BEopt house model. 

Each hot water draw event specifies the flow rate of hot or mixed-temperature water that is 

required to satisfy the draw. Sinks, showers, and baths are assumed to require a minimum “use” 

temperature before the water is used. In this study, a minimum usable hot water temperature of 

105°F was assumed. If, at the beginning of the draw, the fixture outlet temperature is lower than 

that reference temperature, energy and “hot” water are wasted until the fixture outlet temperature 

rises above the minimum use temperature.

2.2 TRNSYS Model Validation

To validate the model, we followed a two-step effort. The first step involved utilizing high 

resolution laboratory data collected on a range of plumbing configurations under controlled 

conditions. Detailed data from laboratory testing completed by Carl Hiller of Applied Energy 

Technology (AET) were used to assess steady-state heat transfer, cold startup delivery 

characteristics, and post-draw cooldown effects. The second step in the validation process 

involved using high resolution distribution system field data collected at the National Renewable 

Energy Laboratory’s (NREL) Solar Row research test house. 

2.3 Distribution Model Pipe Heat Loss Validation

The first step in the validation process was to use AET laboratory test results (Hiller 2006) to 

validate the TRNSYS Type 604 pipe heat loss model during startup and steady-state flow 

conditions. The AET laboratory testing protocol involved running hot water through a serpentine 

pipe array and sensing water temperatures (with immersion probes) at the entrance and exit, as 

well as at regular intervals along the length of pipe. The testing was completed in an 

unconditioned warehouse, and often at night to improve the stability of environmental air and 

mean radiant temperatures (MRTs) during the duration of each test. MRT of the space was not 

directly measured, but was assumed to be equal to air temperature of the space (personal 

communication with Carl Hiller February 2010). Inlet water temperatures and flow rates were 

carefully controlled, with fluctuations in temperature of a few tenths of a degree Fahrenheit

during the course of a single test. 

A TRNSYS model was configured to model various piping configurations tested in the AET lab. 

Copper, cross-linked polyethylene (PEX), and chlorinated polyvinyl chloride (CPVC) pipe cases 

were all modeled with three different pipe diameters, insulated and uninsulated cases, and flow 

 

3 This behavioral concept of waiting for hot water is currently not well understood and potentially important. 

Clearly, showers can only begin when the water is hot. Sink uses are different and subject to both behavioral and 

climate influences. In mild climates, many users may rarely use hot water for sink washing, since cold water 

temperatures may provide adequate user comfort. 
7 

rates ranging from 0.5–4.0 gpm. Material properties assumed in the validation effort for the 

different pipe material types (as well as for pipe insulation) are summarized in Table 3. These 

parameters are inputs to the Type 604 model and directly affect the calculated heat loss. Surface 

emissivity, especially for copper pipe, is an important factor influencing uninsulated pipe radiant 

heat loss, especially under situations where the pipe surface temperatures are high and the 

surrounding environment MRT is low. Uninsulated copper pipe is particularly challenging to 

model, since the emissivity of copper is strongly dependent on the surface condition. New, shiny, 

polished copper can have an emissivity as low as 0.02, while fully oxidized copper can approach 

an emissivity of 0.78 (Kreith 1973). 

Table 3. Pipe and Insulation Material Properties 

Material

Assumed 

Surface 

Emissivity

Thermal 

Conductivity 

(Btu/h-ft-°F)

Specific Heat 

(Btu/lb-°F)

Material

Density 

(lb/in.3)

PEX Pipe 0.91 0.20 0.48 0.032 

Copper Pipe 0.40 232.0 0.092 0.320 

CPVC Pipe 0.91 0.08 0.36 0.055 

Pipe Insulation 0.91 0.03 0.31 0.0023 

Table 4 shows the series of cases from the AET laboratory testing that were used in the initial 

model steady-state pipe heat loss validation. Three different pipe materials, three pipe diameters, 

and two insulation cases (uninsulated and 3⁄4 in.) are included in the range of configurations 

simulated. The insulation conductivity value assumed was derived in prior modeling work where 

the HWSIM model was validated against AET laboratory data (Hoeschele and Weitzel 2012).

Table 4. Summary of AET Configurations and TRNSYS Inputs for Model Validation

Case Tested Pipe Case

Nominal 

Hot 

Water 

Flow 

(gpm)

Total 

Pipe 

Length 

(ft) 

Initial 

Pipe 

Surface

Temp. 

(°F)

Average 

Entering

Water 

Temp. 

(°F)

Average 

Environment 

Temp. 

(°F)

Total Pipe 

Entrained 

Volume 

(gal)

1 PEX 3⁄8 in. uninsulated 1.0 159 53.5 134.9 56.1 0.79

2 PEX 3⁄8 in. insulated 1.0 159 55.6 136.5 55.8 0.79

3 PEX 1⁄2 in. uninsulated 1.0 124 52.6 136.1 54.7 1.14

4 PEX 1⁄2 in. insulated 1.0 124 49.6 115.8 53.0 1.14

5 PEX 3⁄4 in. uninsulated 0.5 89 55.5 135.3 57.2 1.64

6 PEX 3⁄4 in. insulated 0.5 89 54.0 137.2 58.6 1.64

7 PEX 3⁄4 in. insulated 4.0 89 50.4 135.6 51.3 1.64

8 Copper 3⁄4 in. uninsulated 1.0 91 60.7 135.8 62.3 2.30

9 Copper 3⁄4 in. insulated 1.0 91 46.7 136.2 47.5 2.30

10 CPVC 3⁄4 in. uninsulated 1.0 91 70.8 135.8 72.1 1.79

11 CPVC 3⁄4 in. insulated 1.0 91 64.4 135.6 69.9 1.79

 
8 

2.4 Whole-House Model Validation Effort

The second step in the validation process utilized detailed monitoring data from the Solar Row 

project (Barley et al. 2010) to validate “full system” performance. The Solar Row home, located 

in Boulder, Colorado, features a trunk-and-branch plumbing configuration composed of both 1⁄2-

in. and 3⁄4-in. uninsulated PEX piping, with piping located in both conditioned and unconditioned 

space. The “as-built” plumbing layout for the home defined exact pipe lengths, diameters, 

orientations, and locations. At the time of the Solar Row project evaluation, preliminary 

TRNSYS model validation work was undertaken using the older Type 709 pipe model. With the 

updated Type 604 pipe model, each distinct pipe element (defined by diameter, insulation level, 

orientation, and environment) would be modeled with a large number of individual nodes. Initial 

runs with the full “as built” Solar Row distribution system modeled resulted in very long run 

times with the 1000 nodes per segment approach, so a lower resolution 50 nodes per segment 

resolution was used for the evaluation presented here.

Monitoring data from the Solar Row test home from March to June 2008 was used to drive the 

model’s hot water demand, as well as provide environment temperature and water heater outlet 

temperature input data. The dataset included 5-s interval temperature and flow data for each 

fixture as well as mains inlet water temperature and water heater outlet temperature. Ambient air 

temperatures for some pipe environments were monitored on a one minute interval. 

2.5 Extending the Validated Model to Other Climates

The final step in the modeling process, after successful validation against the laboratory and field 

data, was to exercise the model in five different climates to assess the impacts of climate on 

distribution system performance. The plumbing configuration from the 2,010-ft2

 home, shown in 

Figure 1, was input using the Type 604 model. The three-bedroom home was modeled with a 

trunk-and-branch plumbing layout, which is fairly common for much of the country. The 

modeled layout consists of uninsulated copper pipes located in an unconditioned crawlspace. An 

electric water heater, located in semi-conditioned space was also modeled. Model runs were 

completed for five climates (Denver, Chicago, Houston, Phoenix, and Seattle), both with and 

without pipe insulation. 

The model was fed 6-s interval hot water draw data based on loads generated by the Domestic 

Hot Water Event Schedule Generator (Hendron and Burch 2008). Previous analysis of this model 

suggests that a 6-s time step captures the realistic minimum duration of hot water draw events 

while saving some processing time, when compared to 1-s time step (Maguire et al. 2011). The 

discrete draw events are then processed to make a TRNSYS input file that lists draw volumes 

and fixture type indicators (see coding in Table 2) for every time step. A sample day of 6-s 

interval hot water use data is presented in Figure 2. 
9 

Figure 2. Sample 6-s interval hot water draw profile for one day

2.6 Impact of Load Patterns on Distribution System Performance

Distribution system performance is highly dependent on many factors, with a key variable being 

how the occupants consume hot water. The pattern of use, number of draws, and hot water 

volume consumed impact not only the energy losses during hot water draws, but more 

importantly, the losses and water waste that occur between draw events. This latter effect 

represents the bulk of distribution system inefficiencies, since hot water uses that require 

minimum use point temperatures (showers, many bath and kitchen sink uses) will require the 

purging of the entire distribution path if the entrained water is below the use temperature. 

Clustering of hot water draws tends to reduce energy use and water waste, since there is a greater

likelihood that hot water in the lines will be adequate to satisfy the next hot water demand. In 

fact, in the extreme case of highly clustered household hot water use (e.g., all hot water 

consumed from 6:00 a.m. to 8:00 a.m. and 6:00 p.m. to 9:00 p.m.), the widely assumed benefits 

of pipe insulation in nonrecirculating distribution systems would be minimized, since insulation 

offers value primarily in extending the time that usable hot water remains in the pipe (typically 

from 10–15 min without insulation to 30–45 min with insulation). 

This area of research relies on gathering high-resolution hot water usage data (< 5-s interval data 

during flow events) in an effort to develop a statistically valid characterization of hot water draw 

events in terms of timing, duration, end uses, and flow rate.

4 LBNL (Lutz and Melody 2012) has 

been working on developing and populating a hot water use database with data from highresolution hot water end use field monitoring studies. The current status of the available data in 

the database will be reviewed to provide input on the state of knowledge in this area, and what 

additional information is needed to support future improved assessments of hot water distribution 

system performance.

Storage water heaters, whether electric or gas, reliably deliver hot water, with the exception 

being those rare occasions when the storage tank has been fully depleted by prior large hot water 

demands. Although the outlet temperature will fluctuate with the wide dead band associated with 

storage tank aquastats, the leaving water temperature will nearly always be of sufficient 

 

4 Data collected at 1-min or longer intervals will mischaracterize the flow rate and duration of the many short draw 

events that typically occur.
10 

temperature (nominally 105°F) to satisfy end uses such as showering and sink use. The advent of 

gas tankless water heaters over the past 10 years, as well as the emergence of heat pump water 

heaters, may suggest a change in hot water usage as customers interface with products that 

provide different hot water delivery characteristics. This is likely most pronounced with tankless 

water heaters, as a “cold start” situation results in added time for the unit to generate hot water. A 

recent field study where existing gas storage water heaters were retrofitted with advanced gas 

water heaters (including eight gas tankless units) provides detailed monitoring data on observed 

hot water delivery characteristics and usage patterns (Hoeschele and Weitzel 2013). 
11 

3 Results 

3.1 Model Validation With Laboratory Pipe Heat Loss Data

The monitored AET laboratory data for flow rate, inlet water temperature, pipe layout, and 

environmental conditions (see Table 4) were used as inputs to drive the TRNSYS model. Table 5

summarizes the comparison between monitored and simulated results for the following output 

parameters: 

• Steady-state outlet water temperatures

• Elapsed time to achieve 105°F outlet 

• The ratio of “unusable” hot water dumped (< 105°F) to the entrained pipe volume. 

Table 5. Summary of Laboratory and Simulated Results

Case

Steady-State Pipe Outlet 

Temperature

Elapsed Time From 

Cold Start to 105°F 

Outlet

Ratio of 

Wasted Water 

to Entrained 

Pipe Volume

Lab 

(°F)

Model 

(°F)

Delta 

(°F)

Lab 

(s)

Model 

(s)

Delta 

(s) Lab Model

1 PEX 3⁄8 in. U 124.6 126.4 –1.77 89 88 1 1.38 1.36

2 PEX 3⁄8 in. I 133.8 133.7 0.16 66 64 2 1.50 1.46

3 PEX 1⁄2 in. U 126.4 128.1 –1.69 88 86 2 1.15 1.12

4 PEX 1⁄2 in. I 113.5 113.3 0.21 159 157 2 2.07 2.05

5 PEX 3⁄4 in. U

(0.5 gpm) 121.4 122.9 –1.55 296 286 10 1.40 1.35

6 PEX 3⁄4 in. I

(0.5 gpm) 132.4 132.3 0.10 286 296 –10 1.33 1.38

7 PEX 3⁄4 in. I

(4 gpm) 135.1 134.8 0.30 25 27 –2 1.10 1.19

8 Copper 3⁄4 in. U 129.8 129.7 0.05 198 204 –6 1.28 1.32

9 Copper 3⁄4 in. I 133.9 133.4 0.42 188 193 –5 1.21 1.24

10 CPVC 3⁄4 in. U 129.3 129.8 –0.43 156 163 –7 1.11 1.16

11 CPVC 3⁄4 in. I n/a n/a n/a5 139 136 3 1.16 1.14

Note: “U” = uninsulated case, “I” = insulated case

In reviewing Table 5, keep in mind that the inlet hot water temperatures, flow rate, and 

environmental temperatures were not consistent for all test cases. On average, the model 

predicted steady-state outlet temperatures 0.4°F warmer than the monitored data, ranging from 

0.05°–1.77°F different. The largest discrepancy was for the uninsulated PEX pipes that averaged 

1.7°F warmer for the model when compared to the monitored data. The insulated PEX pipes had 

 

5 This test did not reach steady-state outlet temperature before the test was concluded.
12 

an average steady-state temperature difference of 0.1°F, with no deviation greater than 0.21°F.6

Modifications to the assumed uninsulated pipe emissivity yielded little improvement of steadystate temperature differences. (Note: Plastic pipe and pipe insulation surface emissivities are 

fairly well documented to be in the 0.91 range; however, bare copper pipe surface emissivity will 

change significantly as the pipe transitions from shiny new pipe to oxidized older pipe.) 

The elapsed time for the pipe leaving water temperature to reach 105°F was also compared to the 

AET laboratory data. This metric for characterizing pipe heat transfer effects is important to 

accurately estimate hot water waste when draws occur after a long cooldown period. The elapsed 

time results for each configuration varied slightly between model and laboratory data, but in 

general the match was excellent with typical deviations from the laboratory result of < 3.5%. The 

largest percentage difference was observed in the insulated 3⁄4-in. PEX case where the model 

projected a 2-s slower delivery time to achieve 105°F (27 versus 25 s). It is important to look at 

these results in context of the physical flow phenomenon that is occurring in the laboratory and 

may not be fully captured in the model. The final parameter in Table 5 is the ratio of hot water 

dumped (before 105°F outlet is achieved) divided by the pipe volume. This flow characteristic is 

a function of pipe material, flow rate, and temperatures, and varies from slightly more than 1.0 to 

values exceeding 2.0. Intuitively one might expect the hot water waste would be no greater than 

the entrained pipe volume, but both the laboratory data and the model indicate pipe heat 

capacitance and flow effects that increase the waste term based on flow rate, hot inlet 

temperatures, and environment conditions.7

 

Figure 3 below compares the insulated and uninsulated 3⁄8-in. PEX simulation results to 

laboratory data using 1000 nodes for the full length of pipe. From a cold start, the insulated cases 

show an earlier rise in outlet temperature than the corresponding uninsulated case, as less heat is 

lost in transit. For the insulated case, although the temperatures are virtually equal at steady state 

(~3 minutes elapsed), the initial impulse of warm water (at about 0.6 min) is observed earlier in 

the model than in the laboratory. This transition to a steady-state condition appears to be the 

most complex aspect in trying to achieve consistent validation. Interestingly in the uninsulated 

case, where pipe surface heat transfer effects are more critical, the model appears to be 

converging to a 0°F temperature difference at 1.75 min elapsed, but then slowly drifts away and 

stabilizes at an ~2°F differential at 3 min. This pattern of convergence and then separation is 

interesting and potentially suggests changing conditions in the laboratory or pipe flow effects 

whose impacts may be accentuated in narrow 3⁄8-in. diameter piping. 

 

6 All things being equal, one would expect a better match with insulated pipe versus uninsulated pipe, since 

uninsulated pipe will be much more influenced by surface convective and radiant effects, which are challenging to 

control for in all but the most controlled laboratory environments. 

7 This effect is discussed in detail in the Hiller (AET) ASHRAE papers.
13 

Figure 3. Comparison of laboratory and TRNSYS model results for 3⁄8-in. PEX from a cold start 

3.2 Model Validation With Solar Row Field Data

With the successful validation comparison to the AET laboratory data, the Type 604 pipe model 

was next incorporated into the Solar Row as-bulit whole house distribution model. The original 

Type 709 Solar Row TRNSYS model developed by Greg Barker was converted to the Type 604 

using a noding resolution of 50 nodes per pipe segment.8 Collected data from the Solar Row 

project9 included detailed monitoring data of delivered hot water temperature at end use points 

and recording of environment temperatures at some of the areas where hot water piping was run.

One aspect of the simulation was tested with the field data by comparing the volume of water 

wasted before the master shower outlet temperature reached 105°F. The ratio of wasted hot water 

divided by entrained pipe volume was plotted in Figure 4, as a function of time between master 

shower draws. Based on the graph it can be seen that the volume of wasted water, as a ratio of 

total entrained pipe volume, is reduced if the fixture was used within 1 h. 

 

8 The reported simulation results were completed using 50 nodes per pipe segment, since the original 1000 nodes 

required upward of 12 h of simulation time per month. Reducing the number of nodes shortened the run time to < 1 

h while only increasing the May monthly deviation between model and data by 0.02%.

9 www.nrel.gov/docs/fy10osti/48385.pdf 

-15.0

-12.0

-9.0

-6.0

-3.0

0.0

3.0

6.0

9.0

12.0

15.0

18.0

21.0

24.0

27.0

30.0

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

0.000.250.500.751.001.251.501.752.002.252.502.753.00

Temperature DeltaT (Lab-TRNSYS) °F

Pipe Outlet Temperature (°F)

Elapsed Time From Start Of Draw (Minutes)

Lab: Uninsulated

TRNSYS: Uninsulated

Lab: Insulated

TRNSYS: Insulated

Lab - TRNSYS (Uninsulated)

Lab - TRNSYS (Insulated)
14 

Figure 4. Wasted water ratio as a function of time between draws (field data)

The amount of water wasted to reach 105°F at the fixture is ~35% of the entrained water volume 

when showers are taken within 10 min of each other, compared to 130% when showers are taken 

more than 60 min apart. These findings are generally consistent with the AET laboratory results.

For the Solar Row piping configuration, consecutive master bathroom showers would 

potentially10 save 0.8 gal of hot water relative to “cold start” showers whereby entrained water 

would have cooled below a minimum comfort temperature. The data from Figure 4 are binned 

and presented in Figure 5 with average bin values shown above each bar. At time intervals 

between draws greater than 60 min, the ratio approaches the results observed in the AET 

laboratory testing, as shown in Table 5. 

 

10 Depends upon user behavior

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.01 0.1 1 10 100 1000

Wasted Volume to Pipe Volume Ratio

Time Since Last Water Draw at Fixture (hours, logarithmic scale)
15 

Figure 5. Binned wasted water ratio as a function of time between draws (field data)

A second comparison was made to compare monitored and modeled pipe cooldown rates 

between hot water draw events. As shown in Figure 6, the model initially decays slower than the 

monitored data suggest, resulting in negligibly warmer temperatures then indicated by the 

monitored data. At about minute 20 (~15 min after the draw ends), the temperature profiles cross 

and the modeled temperature falls slightly below the monitored data. This pattern of changing 

decay rates over time was consistently observed throughout the dataset. In general, the match is 

very good with minimal impact on the delivered water temperature. Figure 7 shows the decay 

characteristics for a series of kitchen sink draws.

0.006

0.350

0.868

1.041 1.065

1.297

0.00

0.25

0.50

0.75

1.00

1.25

1.50

<5 5 to 10 10 to 20 20 to 30 30 to 60 >60

Wasted to Entrained Hot Water Ratio

Time Since Last Draw at Fixture (minutes)
16 

Figure 6. Comparison of simulated and actual pipe thermal decay over time

Figure 7. Comparison of simulated and actual pipe thermal decay with sequential draws 

0.0

0.5

1.0

1.5

2.0

2.5

70

75

80

85

90

95

100

105

110

115

120

0 5 10 15 20 25 30 35 40 45

Flow Rate (gpm)

Fixture Outlet Temperature (°F)

Elapsed Time (minutes)

Modeled Fixture Outlet Temperatures

Monitored Fixture Outlet Temperatures

Fixture Flow Rate

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

60

70

80

90

100

110

120

130

140

0 5 10 15 20 25 30 35 40 45 50 55 60

Flow Rate (gpm)

Outlet Temperature (°F)

Elapsed Time (minutes)

Modeled Fixture Outlet Temperatures

Monitored Fixture Outlet Temperatures

Fixture Flow Rate
17 

Given the favorable comparison with individual draws against the Solar Row data, the TRNSYS 

model was then used to predict Solar Row system performance over the March to June period 

where hot water draw data, cold water inlet temperatures, and environment temperatures were 

available to drive the model. Distribution losses were calculated and summed monthly. The 

monthly distribution losses shown in Table 6 were found to be within 2% of the monitored losses 

for each of the four months, which is a surprisingly consistent result. Over the four months, the 

total distribution losses ranged from 16%–19% of the energy delivered from the water heater. 

Table 6. Comparison of Observed and Modeled Pipe Heat Losses

Month

Average 

Hot Water 

Use 

(gpd)

Simulated 

(Siml) Pipe 

Losses 

(kBtu/month)

Observed 

(Obs) Pipe 

Losses 

(kBtu/month)

% 

Difference 

(ObsSiml)/ 

Observed)

Pipe Losses as 

% of Total 

Energy 

Delivered 

March 14 41.9 42.0 0.2% –17%

April 28 76.2 75.5 –0.8% –16% 

May 22 61.5 60.5 –1.7% –17% 

June 22 61.4 60.3 –1.7% –19% 

3.3 Distribution System Performance in Different Climates

The final step in the development and validation of the TRNSYS Type 604 model was to 

exercise the validated model on a prototypical distribution system layout in various climates. The 

goals of this effort were to document distribution system performance in different climates and 

assess the energy impact of insulating the entire distribution system. 

Table 7 shows annual energy use and losses for a typical 0.91 energy factor storage electric 

water heater. Water heater tank losses, recovery load, distribution losses, and useful delivered 

energy are reported, as well as daily average hot water usage (gpd) and hot water waste. Hot 

water waste is the energy that is dumped at a non-appliance use point that is of insufficient 

thermal quality (i.e., 105°F). Projected annual electric water heater energy consumption among 

the five locations varies by 64% from the warmest climate (Phoenix) to the coldest climate 

(Chicago). The major factor impacting this large variation is the cold water inlet temperature, 

which influences both the heating energy input as well as the required mixing ratio of hot and 

cold water to achieve the expected 105oF minimum thermal condition (for showers). Distribution 

pipe losses during draws (row E in the Table) vary with climate to a lesser degree, but represent 

a greater percentage of total water heater recovery load in the mildest climates (range from 16%–

23% of total recovery load). The fraction of energy wasted due to temperature decay between 

draw events was found to be higher in the colder climates. The combined “pipe + waste” 

distribution losses were found to be a consistent 26%–27% of the total recovery load, although 

the associated energy ranged from 534 kWh/yr in Phoenix to 892 kWh/yr in Chicago. Wasted 

water heat loss, associated with the time between draws, pipe location, and heat loss environment 

temperatures, were found to be four times greater in cold climates versus the hot Phoenix 

climate. These findings are specific to the simulation inputs, with results being sensitive to usage 

pattern, overall hot water load, user behaviors, and plumbing configuration. 
18 

Table 7. Modeled Results by Climate Zone 

Denver Chicago Houston Phoenix Seattle

A. Water Heater Energy Input

(kWh/yr) 3,698 3,793 2,600 2,307 3,666 

B. Water Heater Tank Losses 

(kWh/yr) 377 376 362 359 383 

C. Water Heater Recovery Load 

(kWh/yr) 3,322 3,417 2,238 1,948 3,283 

D. Wasted Water Losses (kWh/yr) 324 344 124 85 313

E. Distribution Pipe Losses 

(kWh/yr) † 537 548 468 449 541 

F. Useful Energy to Use Points 

(kWh/yr) 2,461 2,526 1,646 1,414 2,429 

Annual Efficiency 

(“F”/“A”) 67% 67% 63% 61% 66% 

G. Hot Water Use 

(gpd) 60.1 60.3 56.6 54.5 60.1 

H. Wasted Hot Water 

(gpd) 13.1 13.3 11.5 11.2 13.2 

% Wasted Water Volume 

(“H”/“G”) 22% 22% 20% 21% 22% 

Losses as a Percentage of Water Heater Recovery Load

% Wasted Energy 

(“D”/“C”) 10% 10% 6% 4% 10% 

% Pipe Loss 

(“E”/“C”) 16% 16% 21% 23% 16% 

% Total Distribution Loss 

((“D+E”)/“C”) 26% 26% 26% 27% 26% 

% Useful

(“F”/“C”) 74% 74% 74% 73% 74% 

† During hot water flow events

The model was then run for each climate with 3⁄4-in. insulation on all pipes to evaluate the energy 

and water savings impact of pipe insulation. The estimated energy and water savings are 

presented in Table 8. Insulation energy savings ranged from 19%–21% of the total distribution 

losses reported in Table 7 (sum of rows D and E), with total savings of 111–170 kWh/yr, or 4%–

5% of annual water heater energy use.11 Wasted water savings, reflecting the reduction in water 

dumped at the beginning of a draw, demonstrated a similar 18%–20% annual reduction, with the 

remaining 1%–2% water savings associated with hotter delivery temperatures. Results are 

sensitive to distribution system configuration, and most importantly to occupant behaviors in 

terms of hot water usage and load pattern. 

 

11 In the recent California Title 24 Standards update, evaluations were completed to assess the cost effectiveness of 

pipe insulation. Modeled performance and life cycle cost calculations resulted in a new requirement for piping 3⁄4 in.

and larger to be insulated. See section 4.1 at the link below: 

www.energy.ca.gov/title24/2013standards/prerulemaking/documents/current/Reports/Residential/Water_Heating/20

13_CASE_R_SEMPRA_Single_Family_DHW_%20Sept_2011.pdf 
19 

Table 8. Modeled Climate Zone Insulation Savings Results

Denver Chicago Houston Phoenix Seattle

Savings 

 (% Savings) 

Savings 

(% Savings) 

Savings

(% 

Savings)

Savings

(% 

Savings)

Savings 

(% Savings) 

Distribution 

kWh/yr 

Savings 

(Wasted 

+Pipe)

166 kWh/yr 

(19%) 

170 

(19%) 

122 

(21%) 

111 

(21%) 

164 

(19%) 

Water Savings 

(gal/yr)

287 gal/yr

(1%)

282 

(1%)

389 

(2%)

452 

(2%)

293 

(1%)

Wasted Water 

Savings 

(gal/yr)

858 gal/yr 

(18%) 

854 

(18%) 

842 

(20%) 

846 

(20%) 

855 

(18%) 

3.4 Summary of Latest Findings on Hot Water Usage and Impact of Gas 

Tankless Water Heaters on Use Patterns

Hot water usage patterns have a significant influence on hot water distribution system 

performance, since the timing and draw characteristics vary. Understanding in this area has 

improved in the past few years, as several larger datasets of high resolution usage data have been 

added to the LBNL database. More work is needed to provide better regional resolution, as well 

as impacts of vintage12 on usage. The recently completed study (Lutz and Melody 2012) 

documents the current findings and develops three representative usage patterns to characterize 

low, medium, and high usage households represented in the database. The database contains 

information from 12 studies representing a total of 159 monitored homes and 33,500 days of 

collected data. A vast majority of the studies represents older homes (pre-2000 vintage), which 

raises the question of how representative the data are for newer homes, which would be expected 

to have a higher level of water efficiency. Average household hot water usage over all samples 

was found to be 54.5 gpd with an average median water heater outlet temperature of 122.7°F.

Table 9 was developed using a statistical cluster analysis technique. Typical usage ranged from 

29 gpd in the low use category to 98 gpd in the high use category. The number of draws 

correspondingly ranged from 45–86/day, although it should be noted that what is represented in 

the field data as a hot water draw may well be due to very short unintentional hot water demands 

that may, for example, be initiated with a single lever faucet.

The usage data provide for a better understanding of usage patterns and draw characteristics. The 

summary data suggests lower usage, lower flow rates, and many more hot water draws than 

assumed in the current DOE service hot water Energy Factor test procedure. This has 

implications for both water heater performance, as well as distribution system performance. Jim 

 

12 Vintage influences appliance, showerhead, and fixture hot water use, although older homes will often have a mix 

of low and high water use appliances, showerheads, and fixtures. 
20 

Lutz is leading efforts within the ASHRAE Standard Project Committee 118.2 to provide input 

on revisions to the test procedure, which will better reflect real world operating conditions. 

Table 9. Summary of Hot Water Database Median Usage Characteristics

Usage 

(gpd)

Number of 

Draws/Day

Average 

Flow Rate 

(gpm)

Draw 

Duration 

(s)

Time Since 

Prior Draw 

(s)

Low Use 29.38 45.22 1.21 20 180 

Medium Use 60.52 66.48 1.21 24 120 

High Use 98.04 86.37 1.16 60 172 

In addition to improving our understanding of hot water use and usage patterns as a key input for 

simulation models, it is important to keep in mind that emerging water heating technologies may 

also influence the consumption of hot water. A technology that has gained significant market 

share over the past 10 years is the gas tankless water heater. This technology has several 

performance attributes that may influence hot water usage: minimum flow rates to activate firing, 

cold start time delays before hot water is generated, and the ability to deliver limitless hot water 

(up to a maximum flow rate). A recently completed California field study (Hoeschele and 

Weitzel 2013) provides a before and after comparison of hot water usage in seven households 

that were converted from conventional gas storage water heaters to gas tankless units. Since that 

work is peripheral to the main research focus of this study, a detail summary is presented in 

Appendix B rather than in the body of the report. A brief summary of the key findings from that 

study follow: 

• On average, 7.2% of total volume of tankless hot water flow was found to be at a 

temperature < 105°F versus 1.2% for the storage water heater cases. This result suggests 

that all things being equal, tankless water heaters will waste more water and increase hot 

water times in comparison to a traditional storage water heater. 

• Overall, for the seven sites, hot water recovery loads increased by an average of 8%.

Although one site in particular appeared to change its usage as evidenced by longer 

showers and greater usage with the tankless water heater, this dataset does not suggest 

that all tankless users will consume more hot water than with a storage water heater. This 

finding is consistent with prior field study research (Schoenbauer et al. 2012). 

• Small draws13, especially when starting with a “cold” tankless unit, are more difficult to 

effectively satisfy, since there will be an increased time delay in delivering the hot water 

to the use point. The field data suggest that on average there is a 23% reduction in hot 

water draw events in the households that converted to tankless units. 

This emerging research on hot water usage patterns and the impact of gas tankless water heater 

on usage provides new information on the expected loads and patterns of hot water consumption. 

Clearly more field data and better data granularity would support the development of improved 

inputs for driving the detailed hot water distribution simulation models. 

 

13 Which may include low flow rate draws that are below the unit’s minimum firing rate.
21 

4 Conclusions 

This research effort focused on extending the state of the art in demonstrating the accuracy and 

usability of advanced hot water distribution system simulation models. 

The primary research questions addressed include:

• How well can detailed distribution system models predict hot water delivery 

temperatures and distribution losses given available input validation data? 

The TRNSYS Type 604 pipe heat loss model was validated against both detailed 

laboratory and field monitoring data. The model aligned well with the laboratory 

dataset, providing a good match in terms of heat loss (as reflected in steady-state 

use point delivery temperature), time required to meet a specified end use delivery 

temperature, and estimation of water waste from a cold start. The latter 

characteristic is especially important in characterizing the waste implications of a 

distribution system as many end uses, such as showers, require a minimum use 

temperature before the event can occur. In comparison to four months of detailed 

field monitoring data from the Solar Row research site, the TRNSYS model of the 

as-built distribution system demonstrated no more than 1.7% monthly deviation in 

pipe heat losses as compared to the monitoring data. 

Modeling results evaluating a prototypical distribution system configuration in 

different climates suggests that for an electric storage water heater distribution 

losses represent about 26%–27% of the annual water heater recovery load, with 

an associated energy use impact of 534–892 kWh/yr. Insulating the full 

distribution system with 3⁄4 in. pipe insulation would reduce the distribution loss 

by 111–170 kWh/yr. Different usage quantities and patterns would affect these 

savings estimates.

• What model limitations are identified as part of this validation effort?

Although the model was found to match well with both datasets, it is fairly time 

consuming to configure, and also requires a short (< 10 s) time step to adequately 

represent hot water draw events. This short time step results in lengthy simulation 

run times and raises the question of how best to integrate a detailed distribution 

system model within the context of overall building energy simulation models, or 

whether it is even necessary. As researchers gain a better understanding of the 

many factors affecting distribution system performance, a better determination 

can be made on whether the effort is warranted in developing a detailed 

distribution system model.

Secondary questions related to the use of these modeling tools include: 

• What new information have we learned on hot water usage quantity and patterns 

(e.g. climate impact, seasonal impacts), how do these revised assumptions affect 

advanced system savings projections, and what information is useful in improving 

modeling algorithms (BEopt, HWSIM, TRNSYS, etc.) and supporting standards 
22 

activities (ASHRAE Standard Project Committee 118.2 and DOE’s Energy Factor 

test procedure update)?

Hot water loads identified in various research studies and aggregated in the LBNL 

hot water database suggest that hot water loads both in terms of gallons per day 

and Btu of recovery load are considerably lower than assumed in DOE’s water 

heater test procedure. The implications of this are significant not only in terms of 

influencing the DOE test procedure revision, but also in comparing alternative 

water heating technologies. Lower loads, more draws per day, and a greater 

seasonality in hot water usage have performance implications for a range of water 

heating system types.

• What conclusions can we draw on customer behavioral hot water use (seven sites) as 

they change from conventional storage water heaters to gas tankless water heaters 

and other advanced system options (to the extent data are available)? 

A controlled field test in California monitored the changes in hot water usage and 

usage pattern at seven households that experienced conversions from gas storage 

water heaters to tankless water heater. At all seven sites, households were found 

to uniformly change usage patterns by reducing the number of daily hot water 

draws (23% on average) and corresponding increases in the average draw volume. 

Small, low flow rate draws were seen to become less frequent in all households as 

users adjusted to the different hot water delivery characteristics of the tankless 

unit water heater. In terms of whether households offset the higher observed 

efficiency of the tankless unit by consuming more hot water, only one of seven 

sites showed a clear increase in the length of typical larger volume shower draws.

This limited sample of test sites suggest that further study is warranted to better 

support or dispute the hypothesis that hot water usage with a tankless unit is 

significantly higher than with a conventional storage water heater. 

4.1 Next Steps

A key recommendation for further research activities would center on refining the inputs needed 

to drive the detailed distribution models and completing more detailed modeling to assess the 

sensitivity of the results to changes in the input assumptions. This information would be useful in 

evaluating whether there is sufficient value in developing a methodology to simplify the 

integration of the detailed TRNSYS model into mainstream simulation applications. 
23 

5 References 

Barley, C. Dennis; Hendron, R.; Magnusson, L. (2010). “Field Test of a DHW Distribution 

System: Temperature and Flow Analyses.” NREL/PR-550-48385. Presented at the 2010 ACEEE 

Hot Water Forum. www.nrel.gov/docs/fy10osti/48385.pdf. 

Hendron, R.; Burch, J. (2008). “Development of Standardized Hot Water Event Schedules for 

Residential Buildings”. NREL/PR-550-40874. Presented at the 2008 Energy Sustainability 2007 

conference. www.nrel.gov/docs/fy08osti/40874.pdf. 

Hiller, C. (2006). “Hot Water Distribution System Piping Time, Water, and Energy Waste - 

Phase I Test Results.” ASHRAE Transactions 114(1):415–425. 

Hoeschele, M.; Weitzel, E. (2013). “Monitored Performance of Advanced Gas Water Heaters in 

California Homes.” ASHRAE Transactions 119(1). 

Hoeschele, M.; Weitzel, E. (2012). Hot Water Distribution System Model Enhancements. 

DOE/GO-102012-3626. Work performed by the Alliance for Residential Building Innovation, 

Davis, CA. Golden, CO: National Renewable Energy Laboratory. November, 2012. 

Lutz, J.; Melody, M. (2012). Typical Hot Water Draw Patterns Based on Field Data. Lawrence 

Berkeley National Laboratory. 

Kreith, F. (1973). Principles of Heat Transfer, Third Edition. New York: Harper and Row. 1973. 

Maguire, J.; Krarti, M.; Fang, X. (2011). “An Analysis Model for Domestic Hot Water 

Distribution Systems,” NREL/CP-5500-51674. www.nrel.gov/docs/fy12osti/51674.pdf.

Schoenbauer, B.; Bohac, D.; Hewitt M. (2012). “Tankless Water Heaters: Do They Really Save 

Energy?”. Proceedings of the 2012 ACEEE Summer Study on Energy Efficiency in Buildings.

Springer, D.; Rainer, L.; Hoeschele, M. (2008). “HWSIM: Development and Validation of a 

Residential Hot Water Distribution System Model.” Proceedings of the 2008 ACEEE Summer 

Study on Energy Efficiency in Buildings. 

TESS (2011). T.E.S.S. Component Libraries for Trnsys v. 17.01 released January 1, 2011. 

Madison,WI. Thermal Energy System Specialists, LLC.
24 

Appendix A: TRNSYS Documentation for Type 604, TESS (2011)

Type 709 documentation provided by TESS LLC; copyright © 2013. All rights reserved. Used 

by permission of TESS LLC.

TYPE 604: BI-DIRECTIONAL NODED PIPE

General Description 

Type604 models a liquid filled pipe that can accommodate flow in either direction. Unlike the standard Type 31 pipe 

model in TRNSYS, Type604 can also consider the effects of the pipe and insulation mass. Type31 treats the pipe 

and insulation as massless, asking the user only to provide a value of the loss coefficient. The model calculates the 

heat loss coefficient based on the fluid properties, the pipe properties, the insulation properties, convection (forced 

and natural) and radiation from the outer surface to the environment. The model assumes that the pipe can be 

characterized by a series of inter-connected, fully-mixed, fluid nodes. This mimics a plug-flow model when the 

number of nodes is high. The model does not allow for flow in both directions at the same time step. Figure 1 shows 

cutaway and end views of the pipe modeled by Type604. 

 

 Figure 1: Cutaway and End Views of the Type604 Pipe 

 

Nomenclature

[-] The number of fluid nodes along the length of the pipe 

[-] The number of pipe or insulation nodes along the length of the pipe. 

[kJ/h.m2.K] The total heat transfer coefficient off the outside of the pipe (pipe wall or insulation jacket) 

[kJ/h.m2.K] The convection heat transfer coefficient off the outside of the pipe (pipe wall or insulation jacket) 

[kJ/h.m2.K] The radiation heat transfer coefficient off the outside of the pipe (pipe wall or insulation jacket) 

[kJ/h.m2.K] The contribution of natural convection to the outside convection coefficient. 

[kJ/h.m2.K] The convection heat transfer coefficient from the inside wall of the pipe. 

[-] The Reynold’s number for the outside of the pipe 

[kg/m3] The density of air 

[m/s] The velocity of air movement across the outside of the pipe 

[m] The outside diameter of the insulation jacket. 

[m] The inside diameter of the pipe 

[m] The inside diameter of the insulation jacket (also the outside diameter of the pipe) 

[m] The outside diameter of the pipe (also the inside diameter of the insulation jacket) 

[kg/m.s] The viscosity of air 

[kg/m.s] The viscosity of the fluid 

[-] The Nusselt number for forced convection from the outside of the pipe 

[-] The Nusselt number for flow on the inside of the pipe 
25 

[-] The Reynolds number 

[-] The Reynolds number for flow on the inside of the pipe 

[-] The Prandtl number 

[-] The Prandtl number for flow on the inside of the pipe 

[kJ/h.m2.K] The contribution of forced convection to the outside convection coefficient. 

[kJ/h.m.K] The thermal conductivity of air 

[kJ/h.m.K] The thermal conductivity of fluid 

[kJ/h.m.K] The thermal conductivity of the pipe material 

[kJ/h.m.K] The thermal conductivity of the insulation material 

[-] The Rayliegh number 

[1/K] The inverse of the film temperature (air temperature at the outside of the pipe) 

[°C] The surface temperature of the insulation jacket 

[°C] The surface temperature of the pipe 

[°C] The environment temperature in which the pipe is immersed. 

[m2/s] The thermal diffusivity of air 

[-] The Nusselt number due to natural convection from a horizontally oriented pipe 

[-] The Nusselt number due to natural convection from a vertically oriented pipe 

[-] The Grashof number 

[m] The length of the pipe 

[m] The length of a fluid node 

[m] The length of a pipe node 

[m] The length of an insulation node 

[-] The Nusselt number due to natural convection 

[kg] The mass of a node 

[kJ/kg.K] The specific heat of a node material 

[kJ/h] Energy transferred into a node 

[kJ/h] Energy transferred out of a node 

[kg/h] The mass flow rate of fluid entering a fluid node from the left hand side 

[kg/h] The mass flow rate of fluid entering a fluid node from the right hand side 

[kg/h] The actual mass flow rate entering a fluid node (the maximum of the left and right flows) 

[kJ/kg.K] The specific heat of fluid 

[°C] The temperature of a fluid node 

[°C] The temperature of the adjacent fluid node to the left 

[°C] The temperature of the adjacent fluid node to the right 

[°C] The temperature of a pipe node 

[°C] The temperature of the adjacent pipe node to the left 
26 

[°C] The temperature of the adjacent pipe node to the right 

[°C] The temperature of an insulation node 

[°C] The temperature of the adjacent insulation node to the left 

[°C] The temperature of the adjacent insulation node to the right 

[m2] The cross sectional area of a fluid node 

[m2] The cross sectional area of an annular pipe node 

[m2] The cross sectional area of an annular insulation node. 

[kJ.K.m2/kJ] The radial thermal resistance of the fluid. 

[kJ.K.m2/kJ] The thermal resistance of the pipe wall 

[kJ.K.m2/kJ] The radial thermal resistance of the pipe material 

[kJ.K.m2/kJ] The radial thermal resistance of the insulation material 

[kJ/h.m2.K] The thermal contact resistance between the pipe wall and insulation 

[C] The temperature of a pipe node 

[C] The temperature of an insulation node 

[m2] The surface area of the inside of a pipe node. 

[m] The inside radius of the pipe 

[m] The outside radius of the pipe 

[m] The inside radius of the insulation jacket 

[m] The outside radius of the insulation jacket. 

Detailed Description 

This routine models the heat loss from a pipe to the surroundings using a network of insulation, pipe, and 

fully-mixed fluid nodes. The user is asked to specify the various physical characteristics of the pipe, 

insulation, and liquid. Specifically: size (length, inner, and outer diameter of the pipe, and outer diameter 

of the insulation), density, thermal conductivity, and specific heat as well as the fluid viscosity is also 

required. The user is also asked to specify the number of axial fluid nodes and the number of axial 

pipe/insulation nodes. While the pipe material and insulation material are treated separately, have 

separate energy balances and separate temperatures, the length of the pipe nodes and the insulation 

nodes are identical. The model does not include radial nodes in the fluid, pipe wall, or insulation and the 

number of axial fluid nodes must be an integer multiple of the number of pipe/insulation nodes so that the 

end boundaries of fluid nodes coincide with the end boundaries of the pipe and insulation nodes. Figure 2 

shows the configuration of the axial fluid nodes above and the of the axial pipe and insulation nodes 

below. In the diagram, there are two fluid nodes for each pipe node, satisfying the requirement shown in 

equation 604.1 

 

, and must be an integer 

(Eq. 604.1) 
27 

Figure 2: Pipe Axial Noding Scheme 

The outside of the pipe insulation jacket is subject to both convection and radiation heat exchange with a 

single environmental temperature. In order to determine the radiation losses, Type604 calls the TESS 

Library long wave radiation exchange routine, which calculates the radiation heat transfer coefficient 

between two surfaces at known temperatures (the surface temperature of the insulation node and the 

environment temperature in this case) based on a user-supplied surface emissivity. Type604 assumes 

that both the insulation surface and the environment to which it radiates have the same emissivity. The 

total convection coefficient for the insulation jacket is determined as: 

(Eq. 604.2) 

The user has two options when it comes to determining the convective heat loss coefficient (houtside,conv); 

the value may be calculated internally or provided by the user. If the user sets the convection mode 

parameter to 1, the model will calculate the convection coefficient based on correlations for either a 

horizontal pipe or a vertical pipe. The orientation of the pipe is again set by the user as a parameter to the 

model. The Reynolds number for forced convection on the outside of the pipe is given by 

(Eq. 604.3) 

The Nusselt number for forced flow is: 

(Eq. 604.4) 

In which the Prandtl number (Pr) is a function of air pressure and a film temperature, which in this case is 

the average of the insulation surface temperature and the surrounding air temperature. In turn, the forced 

convection heat transfer coefficient is given by an algorithm proposed by Churchill and Bernstein [1] 

(Eq. 604.5) 
28 

The 3.6 multiplier in the above equation is used simply to convert the units of the forced convection heat 

transfer coefficient to TRNSYS units of kJ/h.m

2

.K from W/m

2

.K. 

Natural convection for a horizontally oriented pipe is calculated using a second algorithm proposed by 

Churchill and Bernstein [2] by first obtaining the Rayleigh number: 

(Eq. 604.6) 

The Nusselt number for natural convection is 

(Eq. 604.7) 

For a vertically oriented pipe, an algorithm from Lefevre and Ede [3] is used. The Rayleigh number is 

(Eq. 604.8) 

The Grashof number is 

(Eq. 604.9) 

And the Nusselt number is 

(Eq. 604.10) 

The natural convection heat transfer coefficient is then. 

(Eq. 604.11) 

Where Nu

natural

is either Nu

natural,horiz

or Nu

natural,vert. 
29 

Fluid Node Energy Balance 

Type604 performs an energy balance on each fluid node, annular pipe node, and annular insulation node 

in the pipe. The basic form of the energy balance for any given node is: 

(Eq. 604.12) 

If the user wishes to neglect the thermal capacitance of either the pipe wall or the insulation jacket, it 

suffices to set the specific heat or the density of either the pipe wall material or the insulation material to 

0. In this case, the energy balance for a given node of that material becomes: 

(Eq. 604.13) 

For a fluid node, there are three basic terms in the energy balance. Energy transferred into and out of the node 

because of fluid flow, energy transferred due to axial conduction between fluid nodes, and energy transferred 

between the fluid and the pipe wall. Energy transfer due to flow takes the form: 

(Eq. 604.14) 

(Eq. 604.15) 

(Eq. 604.16) 

(Eq. 604.17) 

These four equations indicate that flow can enter the pipe from the “left” end or from the “right” end. Flow 

cannot enter from both ends at the same time. If the user specified mass flow rates into both the “left” and 

“right” ends, the larger of the two flows will dominate and the smaller of the two will be ignored. The actual 

(dominant) flow rate is referred to as mfluid. 

Energy flow due to axial conduction takes the form: 

(Eq. 604.18) 

(Eq. 604.19) 

In which L is the distance between two nodes; since all nodes are equal length, L is also the length of a node. Energy 

flow between the fluid and the wall depends on whether or not fluid is flowing in the pipe. The Reynolds number is 

calculated as 
30 

 

(Eq. 604.22) 

(Eq. 604.23) 

Nusselt is then calculated as 

For Re < 2300 and 

 

(Eq. 

604.24) 

 

For Re < 2300 and (Eq. 

604.25) 

For Re > 2300 and 

(Eq. 

604.26) 

For Re > 2300 and

(Eq. 

604.27) 

The inside convection coefficient follows as 

(Eq. 604.28) 

The general form of the energy transferred between the fluid node and the pipe wall node can finally be 

written as 

(Eq. 604.29) 

where 

(Eq. 604.30) 
31 

(Eq. 604.31) 

It is important to recall that a single pipe node may transfer energy with more than one fluid node. 

Pipe Node Energy Balance 

The annular pipe nodes transfer energy axially with adjacent pipe nodes, with the fluid, and with the 

annular insulation nodes that surround them. Terms for the energy transfer with the fluid have already 

been discussed in the previous section. The terms for axial conduction between nodes take a form similar 

to that of the axial conduction between adjacent fluid nodes: 

(Eq. 604.32) 

(Eq. 604.33) 

Where the annular area (Apipe) is given by: 

(Eq. 604.34) 

The energy transferred between the pipe node and the insulation node can be written as 

(Eq. 604.35) 

Rcontact is a user-defined parameter that accounts for the contact resistance between the pipe wall and the 

insulation. The other two terms (Rpipe, and Rinsul) are given by: 

(Eq. 604.36) 
32 

(Eq. 604.37) 

If the user has set the thickness of the insulation layer to 0, the Rinsuland R

contact terms in equation 604.35 

do not apply and pipe outer wall is subject to external convection in much the same manner as was 

discussed for the outside of the insulation jacket earlier in this document. The outside convection 

coefficient h

outside

is determined using equations 604.2 through 604.11 and is applied to the following 

equation: 

(Eq. 604.38) 

SA in this case is the outside surface area of the pipe node. 

Insulation Node Energy Balance 

The annular insulation nodes transfer energy axially with adjacent insulation nodes, with the pipe 

material, and with the environment. Terms for energy transfer with the pipe have already been discussed 

in the previous section. The terms for axial conduction between nodes take a form similar to that of the 

axial conduction between adjacent nodes: 

(Eq. 604.39) 

(Eq. 604.40) 

Where the annular area (Ainsul) is given by: 

(Eq. 604.41) 

The energy transferred between the insulation node and the environment is written as: 

(Eq. 604.42) 

In which houtside is the result of equation 604.11 and SA is the surface area of the insulation node. 
33 

References

[1] Churchill and Bernstein, Journal of Heat Transfer, no. 99 pg. 300, 1977 

[2] Churchill and Bernstein, “Correlating Equations for Laminar and Turbulent Free Convection from a 

Horizontal Cylinder,” International Journal of Heat and Mass Transfer, no. 18, pg 1049, 1975 
34 

Appendix B: Observed Impact of Gas Tankless Water Heater on 

Hot Water Use Patterns

A 12-month detailed field monitoring project (Hoeschele and Weitzel 2013) was recently 

completed where 18 homes (17 with existing gas storage water heaters) were initially monitored 

and then replaced with advanced gas water heaters (eight14 of which were gas tankless units).

Monitoring included energy use, hot water flow, and water heater inlet and outlet temperature 

sensors. A key project finding that has implications for distribution system performance was that 

observed hot water loads were lower than expected (15.6 gal/person-day). Average household 

usage totaled 56.4 gpd, with a corresponding recovery load of 27,200 Btu/day.15 The lower Btu 

recovery load reflects the milder inlet water temperatures common to most of California. 

The pre- and post-retrofit monitoring protocol employed in the monitoring project provided an 

opportunity to observe how household usage behaviors may change with a conversion to a 

tankless unit. An immersion thermocouple located at the water heater outlet provides an 

assessment of the “quality” of hot water exiting the unit over the 12+ month monitoring period. 

For storage water heaters, one would expect that virtually all water would be “hot” (we define 

105°F as the minimum cutoff for defining an adequate hot water condition), with the likely 

exception being a few cases where high simultaneous loads fully deplete the storage tank. Figure 

8 plots the percentage of the total hot water volume leaving the water heater that was > 105°F.16

As one would expect, the storage water heaters with tank “set points” (average water heater 

leaving temperature) > 120°F demonstrated very little hot water volume below the 105°F quality 

threshold. In most cases < 1% of the volume did not meet the minimum temperature criteria. The 

two storage water heaters with set points of ≤ 115°F did demonstrate a fairly significant (~10%) 

of volume below 105°F. The tankless water data generally exhibited lower average outlet water 

temperatures (in most case < 120°) than the storage water heaters. The lower water temperatures 

appear to affect the percentage of flow < 105°F, probably because the firing control algorithms 

may throttle the initial firing rate if the set point is lower. On average, 7.2% of total volume of 

tankless flow was found to be at a temperature < 105°F versus 1.2% for the storage water heater 

cases. The one monitored tankless unit with a high set point (~150°F)17 demonstrated a lower 

percentage of low-quality output (2.5%), although the volume of substandard hot water was still 

roughly double that of the storage water heaters in general. The data suggest that all things being 

equal, tankless water heaters will waste more water and increase hot water times in comparison 

to a traditional storage water heater. This would appear to be clearly evident for hot water uses 

such as a shower where the minimum acceptable temperature is important in defining the use, 

but may not necessarily be the case for other sink draws where individual behavior dictates if 

105°F water is desired, or if lukewarm or even cold water is acceptable.

 

14 One of the eight tankless retrofits involved replacing a non-condensing tankless unit with a condensing tankless 

unit.

15 The Energy Factor rating test assumes a daily load of 64.3 gal with a recovery load of 41,050 Btu/day. 16 36 data points are plotted since there was pre- and post-retrofit monitoring completed at each site. One of the sites 

had an existing tankless unit install during “pre” monitoring, resulting in nine tankless data points. 17 This high set point was used by the homeowner to address the effect of significant lengths of underslab piping.
35 

Figure 8. Percentage of water heater delivered volume at a temperature > 105°F 

The prior data suggest a greater fraction of hot water flow from a tankless water heater will be < 

105°F than for a gas storage water heater. A related effect is whether there is a general change in 

usage pattern that results from transitioning to a tankless water heater. Small draws, especially 

when starting with a “cold” tankless unit, are more difficult to effectively satisfy, since there will 

be an increased time delay in delivering the hot water to the use point. How individuals in the 

household respond to this delay can affect consumption. Another potential factor is the minimum 

hot water flow rate that is needed to trigger firing in the tankless unit.

18 Low flow rate hot water 

uses (e.g., shaving at a bathroom sink) may require the users to increase the flow rate, or the 

users may just change their use behaviors as they learn the delivery characteristics of the unit. 

Figure 9 and Figure 10 represent small (< 0.5 gal) hot water draw use as a fraction of total hot 

water usage both pre- and post-retrofit. Each site, denoted by a text descriptor for location19 and 

a number (e.g., SD1), demonstrates a post-retrofit reduction in the fraction of small volume 

draws as a percentage of all hot water draws (Figure 9). In some households the effect is more 

pronounced than in others. On average, excluding site PG5 T,

20 the percentage of “< 0.5 gal” 

draws is reduced from 59% in the “pre” data, to 48% in the “post” dataset. In terms of the hot 

water volume associated with the small draws (Figure 10), on average, the data show a reduction 

from 10% of total hot water use down to 6.3% with the tankless unit. The variation among 

 

18 Most tankless units fire at a flow rate of around 0.5 gpm. 19 “SD” = San Diego area, “LA” = Los Angeles area, and “PG” = Northern California Pacific Gas and Electric sites. 20 This site had a tankless water heater in the “pre” period, and was retrofitted with a condensing tankless unit.

0%

2%

4%

6%

8%

10%

12%

14%

16%

100 110 120 130 140 150

% of Water Heater Volume Delivered at < 105F

Average Water Heater Outlet Temperature (F)

Storage Water Heater

Tankless Water Heater
36 

individual sites is significant and speaks to differences in use behaviors among the different 

households. 

Figure 9. Percentage of all hot water draws < 0.5 gallons (pre- and post-tankless retrofit)

0%

10%

20%

30%

40%

50%

60%

70%

80%

SD1 SD3 SD4 LA1 LA4 LA5 PG1 PG5 T Average

% of Hot Water Draws < 0.5 gallons

Site

Pre-Retrofit

Post-Retrofit
37 

Figure 10. Percentage of all hot water volume < 0.5 gal (pre- and post-tankless retrofit)

Figure 11 and Figure 12 present similar plots for large volume draws, which are defined as > 5 

gal. Most of these draws will likely be shower events, although tub uses and extended kitchen 

sink dishwashing may also be included. Figure 11 demonstrates that as the number of small 

draws decrease with the tankless conversion, the average percentage of larger volume draws 

increased from 6.8% of all draws with storage water heaters to 10% with tankless. Figure 12

plots the volume associated with these large draws for each of the sites. With the storage water 

heaters, the large volume draws represent an average of 53% of all hot water consumed, which 

increases to 62% for the tankless units. 

0%

2%

4%

6%

8%

10%

12%

14%

16%

SD1 SD3 SD4 LA1 LA4 LA5 PG1 PG5 T Average

% of Hot Water Volume Used < 0.5 gallon draws

Site

Pre-Retrofit

Post-Retrofit
38 

Figure 11. Percentage of all hot water draws > 5 gal (pre- and post-tankless retrofit)

Figure 12. Percentage of all hot water volume > 5 gal (pre- and post-tankless retrofit)

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

SD1 SD3 SD4 LA1 LA4 LA5 PG1 PG5 T Average

% of Hot Water Draws > 5 gallons

Site

Pre-Retrofit

Post-Retrofit

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

SD1 SD3 SD4 LA1 LA4 LA5 PG1 PG5 T Average

% of Hot Water Volume Used >5 gallon draws

Site

Pre-Retrofit

Post-Retrofit
39 

In summary, as users moved from gas storage water heaters to gas tankless units, their observed 

usage changed significantly. Smaller volume draws, many of which are difficult to satisfy due to 

either flow rate or startup time delays, were found to be significantly reduced, with an overall 

23% reduction in daily hot water draws as households switched to the tankless unit. Countering 

this reduction was an increase in average hot water draw volume size from 1.40–2.09 gal. In 

terms of water heater recovery load, Table 10 shows that in aggregate, the average recovery load 

during the pre- and post- periods was 8% higher with the tankless unit. While two sites (SD1 and 

SD4) appear to have significantly increased hot water consumption, the remaining five sites 

show small changes in average daily recovery load. A final review of larger volume draws 

representative of shower events (Figure 13) suggests that with the exception of site SD4, none of 

the sites indicated a significant change in shower length that would explain increased usage.

While SD4 may represent a household that did take advantage of the tankless “endless hot 

water” marketing claims, it appears that most of the households did not increase consumption, 

although usage patterns were clearly affected.

Table 10. Monitored Pre- and Post-Retrofit Daily Recovery Loads by Site

Site

Recovery Load 

(Btu/day)

% 

Difference

Post Versus

Pre

Comments PreRetrofit

PostRetrofit

PG1 22,600 22,200 –2%

LA1 29,200 28,100 –4%

LA4 8,900 9,800 +10%

LA5 31,600 31,000 –2% Data through April 9, 2011

SD1 17,500 23,800 +36%

SD3 9,400 10,200 +9% Includes some buffer tank heating

SD4 11,400 16,200 +42%

Average 18,700 20,200 +8%
40 

Figure 13. Average shower draw duration (pre- and post-tankless retrofit).

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

LA1 LA4 LA5 SD1 SD3 SD4 PG1 Average

Average Shower Duration (minutes)

Site

Pre-Retrofit

Post-Retrofit
DOE/GO-102013-3945 ▪ July 2013

Printed with a renewable-source ink on paper containing at 

least 50% wastepaper, including 10% post-consumer waste.