A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Development of Standardized 

Domestic Hot Water Event 

Schedules for Residential 

Buildings 

R. Hendron and J. Burch 

Presented at Energy Sustainability 2007 

Long Beach, California 

June 27–30, 2007

Conference Paper 

NREL/CP-550-40874 

August 2008 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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DEVELOPMENT OF STANDARDIZED DOMESTIC HOT WATER 

EVENT SCHEDULES FOR RESIDENTIAL BUILDINGS 

Robert Hendron 

National Renewable Energy Laboratory 

Golden, CO 

Jay Burch 

National Renewable Energy Laboratory 

Golden, CO 

ABSTRACT 

 The Building America Research Benchmark is a standard 

house definition created as a point of reference for tracking 

progress toward multi-year energy savings targets. As part of 

its development, the National Renewable Energy Laboratory 

(NREL) has established a set of domestic hot water events to 

be used in conjunction with sub-hourly analysis of advanced 

hot water systems. In certain applications (including analysis 

of recirculation loops, distribution losses, tankless gas water 

heaters, and solar hot water systems with load-side heat 

exchangers) energy use can be strongly affected by event flow 

rate, duration, frequency, clustering, and time of occurrence. 

High-level constraints on mains temperature and average daily 

hot water use, along with detailed event characteristics derived 

from past research and a software tool developed by Kassel 

University in Germany, were used to generate hot water 

events over one year for houses of different sizes in various 

locations. The events were established in 6-min increments for 

showers, baths, sinks, clothes washers, and dishwashers. Flow 

rates and times of occurrence varied randomly based on 

specified probability distributions. The final event schedules 

reflected the same daily variability as an actual household, 

thereby providing more realism to energy simulations 

involving advanced water heating systems. 

NOMENCLATURE 

day# Julian day of the year (1-365) 

lag 35 – 1.0 (Tamb,avg – 44) 

Nbr Number of bedrooms 

offset 6°F 

ratio 0.4 + 0.01 (Tamb,avg – 44) 

Tmains Mains (supply) temperature to domestic hot 

water tank (oF) 

Tamb,avg Annual average ambient air temperature (oF) 

Vd,avg Annual average volume of hot water (gal) 

Vd,wk-day Daily volume of hot water: weekday (gal) 

Vd,wk-end Daily volume of hot water: weekend (gal) 

Vh Hourly volume of hot water (gal) 

ΔTamb,max Maximum difference between monthly 

average ambient temperatures (oF) 

Δtdraw Hot water draw duration (sec) 

Δttime-step Simulation time step (sec) 

μfr Average event flow rate (l/hr) 

σfr Flow rate standard deviation (l/hr) 

INTRODUCTION 

 The Building America Program sponsored by the U.S. 

Department of Energy (DOE) has defined a series of climatedependent whole-house energy savings goals (e.g., achieve 

40% savings in hot/dry climates by 2007), eventually leading 

to cost-neutral zero-energy homes in all climates by 2020. 

Tracking progress toward these goals necessitates the use of a 

“Benchmark” house. The Benchmark defines climatedependent building characteristics and operational schedules, 

including those for domestic hot water (DHW) use [1]. The 

DHW time-of-day use profiles are based in part on the EndUse Load and Consumer Assessment Program (ELCAP) study 

conducted in the Pacific Northwest [2], and the average daily 

volume is a function of the number of bedrooms, the climate, 

and the season. 

 However, DHW use is quite complex, involving a 

spectrum of users and applications with varying inlet 

temperatures, volumes, flow rates, and timing. The major 

residential DHW end uses include sinks, showers, baths, 

dishwashers, and clothes washers, which are “discrete events” 

initiated by occupants. These events each have a characteristic 

range of flow rates and durations (or volumes), as in [3]. 

DHW use is also idiosyncratic by household. Average daily 

volume usage varies between households by an order of 

magnitude (~50 l/day to ~500 l/day), depending on occupant 

density and their habits [4]. Time-of-use varies widely also. 

For each household, there is both a deterministic component 

and a random component to DHW use. People tend to take 

showers, shave, clean dishes, etc. at more-or-less the same 

time each day, albeit somewhat differently on work-days vs. 

non-work days. On the other hand, DHW usage varies 

1 
2 

stochastically with all the complexities of modern life, 

including somewhat random unoccupied/vacation periods, 

varying number of showers/person/day, varying number of 

loads of clothes and cycle temperatures, varying use of 

dishwashers, somewhat varying duration and flow rates for 

sink and shower events, and so on. 

 To predict DHW energy consumption using a simulation, 

an exogenous hot water draw profile must be chosen. 

However, the level of detail needed in the draw profile 

depends on the study objectives, as described in Table 1. The 

key draw parameters are the average daily volume, draw flow 

rates, timing of draws, and variability in draw patterns. When 

comparing storage water heaters without concern for runout 

(i.e., failure to deliver hot-enough water), the only parameter 

of any significance is the daily draw volume; draw flow rate 

and timing are mostly irrelevant. However, the performance of 

some DHW system components can also depend on the draw 

flow rate, timing, and/or day-to-day variability in load volume. 

The performance of a solar water heater (SWH) is sensitive to 

general load timing (especially integral-collector-storage 

systems) and to day-to-day variability (because of storage 

dynamics). In addition, the performance of a SWH with a 

load-side heat exchanger depends significantly on the flow 

rates of the hot water draws [5]. The analysis of losses from 

DHW distribution sub-systems also depends on the time 

between draws, as in [6]. For these cases it is necessary to 

specify a statistically realistic distribution for the key 

variables. 

 Most previous DHW system studies neglect variability 

and have employed relatively simple draw profiles. Timing is 

ignored in [7]. The schedule used in the U.S. for water heater 

testing [8] is defined as six draws of 11-gallons each, one hour 

apart, with a flow rate of 3 gpm. This schedule should be used 

only when Vd,avg is the only significant system variable, as 

with conventional storage water heaters. A simple 24-hour 

“double-humped” schedule with the flow rate set to Vh/hour is 

used for annual solar hot water system ratings by the Solar 

Rating and Certification Corporation (SRCC) [9]. There are at 

least four studies that have derived ensemble long-term 

averages for Vh [10], but there are very few differences among 

them. These hourly schedules work well if volume and timing 

are of concern, but flow rate and variability are not (as when 

comparing solar systems without load-side heat exchangers). 

To incorporate flow rate dependence, a constrained schedule 

was developed in [11]. Three flow rates were defined: 0.5 gpm 

(representing sink usage), 1.5 gpm (representing showers and 

dishwashers), and 2.5 gpm (representing baths and clothes 

washers). Two schedules (“yuppie” and “nuclear family”) 

were then assembled by defining a set of events (one of the 

three flow rates, start time, and duration of draw) subject to 

the following constraints: Vd,avg = 62 gal/day; the flow rate 

distribution roughly matches that observed in a previous 12-

house study [12]; there are weekday and weekend day types, 

with Vd,wk-end = 1.45 Vd,wk-day; and modulo(Δtdraw, Δttime-step) = 0. 

Similar profiles were developed in [13]. However, such 

profiles do not incorporate day-to-day variability and are 

misleading in predicting absolute performance of or 

optimizing SWH systems. The difficulty of defining 

“realistic” draw profiles led one group to incorporate houseyears of raw data as a draw profile option [14]. This latter 

approach is cumbersome and begs the question of how 

representative those arbitrarily-chosen 10 houses are. 

 The technical objective for the present study is to use 

published data related to average water volume, timing, 

variability, and frequency from previous studies to constrain 

otherwise randomly generated hot water events that can be 

used for all of the applications described in Table 1. 

Table 1 Study Objective vs Draw Characteristics 

Needed in the Draw Profile: Examples 

Study Objective 

Draw Characteristics 

Needed 

Comparing storage 

water heater energya Daily volume only 

Sizing of storage water 

heaterb Timing, volume (“worst” draw sequence) 

Compare SWH with 

load-side heat 

exchangerc 

Volume, flow rate, timing, 

and variability 

Minimize distribution 

network lossesd Volume, flow rate and timing 

a

 With no consideration of runout, as in [7] b

 Runout is the key issue, as in [15] 

c

 Load-side heat exchanger effectiveness depends on flow 

rate, as in [5] 

d

 Compare recirculation types, minimize stranded water, as 

in [6] 

TECHNICAL APPROACH 

Current Benchmark Approach 

 Until 2007, the Building America Benchmark used a 

relatively simple approach to defining hot water use. An 

essentially fixed daily volume was used for each of the 365 

days in a year, with adjustments to the hot water use for 

showers, baths, and sinks, to reflect seasonal variations in 

mains temperature assuming a constant 120oF hot water 

supply temperature. Colder mains temperatures in winter 

would result in higher hot water use to maintain the same 

assumed mixed temperature of 105oF at the fixture. The mains 

temperature was calculated using Equation 1, which was 

developed based on a correlation derived from measured field 

data. The development of this equation is discussed further in 

[1]. An example mains temperature profile for Chicago is 

shown in Fig. 1. 

Tmains = (Tamb,avg + offset) + ratio * (ΔTamb,max / 2) * 

sin (0.986 * (day# - 15 - lag) - 90) (1) 
3 

Using TMY2 data for Chicago, IL

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12

Month

Temperature (F) & Gallons/day

T ambient

T mains

DHW gal/day

BA - Monthly Mains Water Temperature and DHW Usage

Fig. 1 Seasonal mains temperature profile for Chicago 

 The daily average hot water use for the Benchmark was 

established as a linear function of the number bedrooms, 

which served as a surrogate variable for number of occupants. 

The relationship between bedrooms and occupants was based 

on the DOE’s 2001 Residential Energy Consumption Survey 

(RECS) [16]. Baths and showers were treated as one end-use 

until 2007, when they were split as part of this project because 

of their very different hourly profiles. Average daily volume 

for each end-use was established based on a survey of recent 

hot water studies and other authoritative references [4, 17-22]. 

These Benchmark daily hot water volumes are summarized in 

Table 2. 

 

Table 2 Benchmark water temperature and volume for 

each DHW end-use 

End Use 

End-Use 

Water 

Temperature Water Usage 

Clothes 

Washer 

120°F 

(Hot) 

7.5 + 2.5 x Nbr gal/day 

(Hot Only) 

Dishwasher 120°F 

(Hot) 

2.5 + 0.833 x Nbr gal/day 

(Hot Only) 

Shower 105°F 

(Mixed) 

14.0 + 4.67 x Nbr gal/day 

(Hot + Cold) 

Bath 105°F 

(Mixed) 

3.5 + 1.17 x Nbr gal/day 

(Hot + Cold) 

Sinks 105°F 

(Mixed) 

12.5 + 4.16 x Nbr gal/day 

(Hot + Cold) 

 A whole-house normalized DHW hourly profile was 

specified based on [23], and is shown in Fig. 2. More detailed 

end-use hourly profiles for clothes washers and dishwashers 

were established based on the ELCAP study [2]. A single 

hourly profile was used for all other end-uses, calculated by 

subtracting the clothes washer and dishwasher profiles from 

the combined hot water profile, assuming a typical 3-bedroom 

house in Memphis. 

 In most cases this level of detail was sufficient for 

accurate energy analysis of hot water systems. As more 

advanced techniques for reducing water heating energy 

became more commonplace in the quest for affordable Zero 

Energy Homes, it was clear that the Benchmark DHW 

specifications were overly simplistic and would not produce 

accurate results in many cases. However, because the BA 

Benchmark represented the best available information that we 

could find on average water use, we decided to use the highlevel parameters established for the Benchmark (hourly 

profiles, average daily volume) as constraints on the more 

detailed event schedules that we developed for this project. 

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour of Day

Fraction of Total Daily Usage

 Clothes washer and dishwasher event characteristics are 

strongly influenced by the efficiency of appliances present in 

Fig. 2 Combined domestic hot water use profile 

for the Benchmark, representing average use 

Drivers of DHW Events

 The most important driver of hot water events is occupant 

behavior. Differing habits among individuals, along with 

random variations for the same individuals, combine to 

produce highly variable patterns within a specific household. 

It is not our intent to capture family-to-family differences in 

behavior for this project, but simply the variability for a 

typical family. The randomness of occupant behavior can best 

be duplicated using software that uses a random number 

generator driven by realistic probability distributions and other 

high-level constraints established for the Benchmark. 

 Other drivers of event schedules are more predictable. 

The number of occupants is assumed to have a linear effect on 

total hot water use, as discussed in the previous section. This 

variable primarily affects frequency of events, assuming the 

typical behavior of each family member is about the same, 

including average event timing, duration, and flow rate. It can 

be argued that additional family members beyond the first two 

are likely to be children and may use less hot water, but data 

from the 1997 RECS [18] indicate that the relationship 

between average number of occupants and average hot water 

use is fairly linear (Fig. 3). 

 Climate and season also affect event schedules indirectly 

through mains temperature, as discussed earlier. The higher 

the mains temperature, the smaller the flow rate of hot water 

necessary to obtain the desired mixed temperature for enduses such as showers, baths, and sinks. Our assumption is that 

most common clothes washers and dishwashers do not adjust 

the hot water flow rate or volume based on the cold water 

temperature, though we recognize that many advanced clothes 

washers control the mixed water temperature using a 

thermostatic control valve. Dishwashers typically rely on hot 

water only, which is raised to a higher temperature by an 

internal heater. Some references suggest there is seasonality to 

the frequency of hot water events, but that possible 

relationship was not addressed by this study. 
4 

th house being analyzed. Most efficient appliances reduce e 

energy consumption by reducing the amount of water 

necessary to wash the dishes or clothes, by either reducing the 

number of cycles or using less water for each cycle. Usually, 

this results in shorter event durations. Alternate control types 

and adaptive temperature/load size features can also affect the 

event characteristics in many different ways. Because our 

focus was on developing standard event schedules, and 

because of the unlimited possibilities for clothes washer and 

dishwasher events, we assumed typical appliance 

characteristics and left it to the analyst to make the necessary 

adjustments based on actual appliance features. 

0

20

40

60

80

100

120

140

160

12345

MBtu/yr

Number of Occupants

Energy Use by Occupancy

Single Family Detached, 1990s Vintage, All Fuels

Space Heating

Space Cooling

Water Heating

Appliances

Refrigerators

Dishwashers

Total End-Uses

Fig. 3 Relationship between number of occupants and 

energy use for major end-us ed on the 1997 RECS 

xamined as part of this study because water supplied at such 

ev

ution 

st

nt schedules were generated using a software tool 

calc developed by the solar energy research group 

s are optimal for the purpose of 

ations (Table 3), longer 6-min 

me

acteristics in 

1-Min Increments for a Three-Bedroom House 

es bas

The effect of hot water setpoints above 120oF were not

e

el ated temperatures poses a significant risk of scalding and 

is usually not recommended for residential systems. For solar 

storage tanks that supply hot water at temperatures up to 

160oF, the volume and flow rate of hot water events in this 

study should be interpreted as hot water tempered to 120oF 

using either a tempering valve or a second storage tank. 

 Finally, a very important driver of event duration is the 

magnitude of hot water distribution losses. If the distrib

sy em has long pipe runs, oversized pipe diameters, or large 

pipe losses upstream of a fixture, it is likely that the user will 

need to wait a significant length of time before hot water 

arrives at the fixture. In many cases this extends the duration 

of the hot water event, but not always. Appliance hot water 

use is typically unaffected by distribution losses, and many 

people will begin washing their hands before hot water arrives 

at the faucet. Another complication is that the event schedules 

themselves have a large effect on the distribution losses, 

making the analysis an iterative process. Because these 

interactions are very complex, we decided to postpone the 

inclusion of distribution losses in the development of event 

schedules until the next phase of this project. 

Software 

 The eve

called DHW

at Kassel University in Germany [24]. The software can be 

downloaded at no charge from the University’s website 

(www.solar.uni-kassel.de). DHWcalc generates random event 

schedules based on hourly profiles, average event 

characteristics, average daily volumes, and other constraints 

entered by the user. Events have a fixed duration, which must 

be a multiple of the selected time step. Event flow rate, and 

consequently event volume, varies randomly around an 

average value (μfr) based on a standard deviation (σfr) entered 

by the user. Of course in a real household, it is event duration 

that varies much more than flow rate, but it would be 

impossible to have highly variable durations without 

extremely small time steps. An hourly probability distribution 

is entered into the software using a step function. The 

probability distribution is tuned automatically by the software 

to account for user-specified variations in seasonal, 

weekend/weekday, and holiday event frequency. 

Assumptions and Inputs 

 Although 1-min time-step

specifying realistic event dur

ti steps with clustered sink events are actually more 

realistic than independent 1-minute events, because in many 

home situations (such as washing hands before dinner, rinsing 

dishes, getting ready for school/work) there is a tendency for a 

series of sink events to occur at about the same time (see Fig. 

4). Either profile is possible depending on the behavior of the 

occupants, but based on our examination of data collected for 

approximately 1200 households by the American Water 

Works Association (AWWA) [25], clustered sink events 

appear to be more common (see Fig. 5). This conclusion is 

also supported by our recent monitoring activities in Building 

America houses. Fig. 6 shows the clustering of what we 

believe are sink events on a representative day in January for a 

Building America house near Denver, Colorado. In the end, 6-

min time steps were chosen for this project because they 

offered the greatest overall realism. 

Table 3 DHW Event Char

Clothes DishWasher washer Showera Batha Sinka

Vd,avg in ters 

(gal) 

 li 57 

(15) 

19 

(5) 

106 

(28) 

26 

(7) 

95 

(25) 

μfr in l/hr 

(gal/min) 

319 

(1.4) 

242 

(1.1) 

479 

(2.1) 

958 

(4.2) 

161 

(0.7) 

σfr in l/hr 

(gal/min) 

160 

(0.7) 

36 

(0.2) 

239 

(1.1) 

239 

(1.1) 

241 

(1.1) 

Duration (min) 10 8 8 5 1 

Weekend/ 

Weekday ratio 115% 115% 115% 300% 115% 

Events/day 1.07 0.59 1.66 0.324 35.5 

Events/year 392 215 606 118 12958 

a

 Flow rates and vo es in e both ho nd c ate

stics 

ere necessary to make the other four end-uses compatible 

ith

lum clud t a old w r 

 Several adjustments to the 1-min event characteri

w

w 6-min time steps. The duration of showers (8-min) and 

baths (5-min) were changed to 6-min, while keeping the flow 
5 

rate and average daily volume the same. As a result, event 

frequency was altered to some extent for the 6-min time-steps. 

Clothes washer event duration was changed from 10 min to 12 

min, and dishwasher event duration was changed from 8 min 

to 6 min. Because the annual number of clothes washer and 

dishwasher events were defined in the Benchmark, we 

adjusted the flow rates to force the daily volumes to remain 

the same. 

Independent Random Events

Time of Day

Clustered Events

Time of Day

Fig. 4 Comparison of 24 independent random 1-minute 

events to 4 randomly distributed 6-minute clusters 

‐

1

2

3

4

5

6

7

8

12:00 AM

1:00 AM2:00 AM3:00 AM4:00 AM5:00 AM6:00 AM7:00 AM8:00 AM9:00 AM

10:00 AM11:00 AM

12:00 PM

1:00 PM2:00 PM3:00 PM4:00 PM5:00 PM6:00 PM7:00 PM8:00 PM9:00 PM

10:00 PM

11:00 PM

12:00 AM

Numberof Events

Fig. 5 Clustering of faucet events (hot and cold) in 15-

minute intervals for a household with two occupants [25] 

0

1

2

3

4

5

6

7

8

2400 0200 0400 0600 0800 1000 1200 1400 1600 1800 2000 2200

Liters/minute

Hour

DHW 1 Minute Data

Janua , 2007

Fig. 6 Clustering of likely faucet events (hot water only) for 

a Building America house with three occupants 

 T ing 

America using 6-min time-ste are shown in Table 4. Note 

at

aracteristics in 

6-Min Increments for a Three-Bedroom House 

ry 15

he final event characteristics developed for Build

ps

th for the purpose of DHWcalc analysis, sinks, baths, and 

shower events include both hot and cold water. The use of 

combined hot and cold water volume allows climatedependent variations based on mains temperature to be 

ignored during the initial stages of analysis, avoiding the 

necessity to run DHWcalc for many different locations. A 

spreadsheet was designed to automatically extract the hot 

water flow from the DHWcalc outputs based on a userselected typical meteorological year (TMY2) site [26]. 

Seasonal variations in hot water use are also calculated 

automatically by the spreadsheet. 

Table 4 DHW Event Ch

Clothes DishShowerb Bathb Sinkb Washer washer 

Vd,avg in ters 

(gallons

 li

) 

57 

(15) 

19 

(5) 

106 

(28) 

26 

(7) 

95 

(25)

μfr in l/hr 

(gal/min) 

266 

(1.2) 

323 

(1.4) (4.

479 

(2.1) 

958 

2) 

161 

(0.7) 

σfr in l/hr 

(gal/min) 

133 

(0.6) 

48 

(0.2) 

239 

(1.1) 

239 

(1.1) 

241 

(1.1) 

Duration (mins) 12 6 6 6 6 

Weekend/ 

Weekday ratio 115% 115% 115% 300% 115% 

Events/day 1.07 0.59 2.21 0.27 5.9 

Events/year 392 215 808 99 2160 

clude both b

 Flow rates and es in nd co ate

the 

enchmark values shown in Table 2 for a three-bedroom 

u

week in June and one 

e

volum hot a ld w r 

 Average daily volume was constrained to match 

B

ho se. For clothes washers and dishwashers, the hourly 

profiles were taken from the Benchmark, and the event 

frequencies were based on the corresponding DOE appliance 

rating procedures [27, 28]. Average clothes washer and 

dishwasher event durations were estimated by NREL based on 

engineering judgment and some limited appliance testing. 

Flow rates were then calculated based on event frequency, 

duration, and average daily volume. For showers, baths, and 

sinks, the hourly profiles and average event durations and flow 

rates were estimated based on the AWWA study [25]. Event 

frequencies were then calculated based on flow rate, duration, 

and daily volume. It should be noted that the AWWA data 

included all water events (hot, cold, and mixed), and 

individual end-use events were disaggregated statistically 

instead of being measured directly. As a result, the data from 

that study must be used with caution. 

 An assumption was made that a typical home is 

unoccupied 14 days each year (one 

we k in December), and that no events occur on those days. 

This number of unoccupied days (or holidays) was calculated 

based on the average percent of days with no toilet draws in 

the AWWA study. The ratio of average daily volume on 

weekends compared to weekdays was also based on the 

AWWA study. Only bath events were significantly more or 

less common on weekends, occurring approximately three 

times more often than on weekdays. Finally, standard 

deviation of event volume for each end-use was derived from 
6 

dules 

schedules generated for two, three, and 

 have been posted in spreadsheet format 

the water use data in the AWWA study and was intended to be 

representative of an average family. These values may be 

somewhat smaller than the standard deviation for hot water 

alone, because there is likely to be some variation in the enduse temperature superimposed on the variations in duration 

and flow rate that are reflected in the AWWA data. 

RESULTS 

Event Sche

 The final event 

four-bedroom houses

on the Building America performance analysis website 

(www.eere.energy.gov/buildings/building_america/pa_resourc

es.html). The nearest TMY2 site must be selected in the tab 

labeled “Inputs.” In most cases the combined event schedule is

sufficient for the analysis of DHW systems, but individual 

end-use events are also provided in case the events must be 

associated with specific fixtures, as would be the case when 

hot water distribution losses are analyzed. The annual events 

are split into 12 months because the 2003 version of Microsoft 

Excel does not allow 87,000 rows. This limitation will be 

lifted in the 2007 release of Excel. 

 An example set of events for a three-bedroom house in 

Las Vegas is shown in Fig. 7. The graph covers the period of 

March 1-8. Clustering of events and variations in event 

volume were visible, as we would expect in an actual 

household. 

0

100

200

300

400

500

600

700

800

900

1000

60 61 62 63 64 65 66 67

Event Flow Rate (L/hr)

Day of  the Year

Sink Events (L/hr)

Shower Events (L/hr)

Bath Events (L/hr)

CW Events(L/hr)

DW Events (L/hr)

Fig. 7 Final event schedule for the time period March 1-8 

for a three-bedroom house in Las Vegas 

Validatio

vent schedules were generated, we compared 

ent characteristics with the inputs entered into the 

on of this 

ail

characteristics for a three-bedroom house 

n 

 Once the e

the output ev

DHWcalc software to make sure the results were consistent. 

Table 5 summarizes the validation of several important event 

characteristics for the five end-uses, including average flow 

rate (μfr), standard deviation of flow rate (σfr), event 

frequency, and annual water use. Most parameters matched up 

very well except for σfr, which was smaller than we specified 

for all five end-uses. In other words, there was not as much 

event-to-event variability as we wanted. Another concern was 

that sink event characteristics were not consistent with the 

input parameters, except for the annual flow volume. We have 

contacted the creators of DHWcalc to find the cause of these 

discrepancies and to make corrections if warranted. 

 The day-to-day standard deviation in total hot water use is 

about 60% over the course of a year. An illustrati

d y variation for the month of June is shown in Fig. 8. This 

result is somewhat less than the standard deviation of 71% 

calculated based on the AWWA data for almost 1200 houses. 

However, because the AWWA data includes both hot and cold 

water use, it is unclear how this would translate to hot water 

alone. Our supposition is that the variability of hot water 

would be higher for the same reasons discussed earlier in the 

context of event volume. The AWWA data is also for only 4 

weeks of the year, usually 2 weeks in summer and 2 weeks in 

winter. It seems likely that the variability for a whole year 

would be smaller than for 2 weeks at the extremes. Another 

data point is offered by four occupied Building America 

houses with direct monitoring of DHW usage. The average 

day-to-day standard deviation for these four houses was 60%, 

which happens to be the same as our event schedules. A third 

study [12] of twelve houses concluded that day-to-day 

standard deviation was 33% for weekdays and 40% for 

weekends, suggesting that daily volume is significantly less 

variable than the daily sum of event volumes for this project. 

Overall, we are comfortable that our day-to-day variability is 

reasonable compared to the average of other studies. 

Table 5 Validation of key event schedule 

Clothes DishShowera Batha Sinka Washer washer

Input μfr, 1/hr 

(gal/min) (1.17) (1.42) (2.11) (4.22) (0.71) 

266 323 479 958 161 

Output μfr, l/hr 

(gal/min) (1.18) (1.43) (2.13) (4.29) (0.93) 

268 324 484 974 212 

Input σfr, l/hr 

(gal/min) (0.59) (0.21) (1.05) (1.05) (1.06) 

133 48 239 239 241 

Output σfr, l/hr 

(gal/min) (0.33) (0.15) (0.63) (0.77) (0.55) 

76 35 143 175 125 

Input 

events/yr 392 215 799 99 2160 

Output 

events/yr 388 215 808 98 1637 

Input annua

volume, lite

l 

rs 20,

(5,496) 

6935 

(1,832) 

38,

(10,220) 

9490 

(2,507) 

34,

(9,160) (gal) 

805 690 675 

Output annual

volume, liters (5,497) (1,838) (10,221) (2,523) (9,160) (gal) 

20,808 6959 38,692 9550 34,675 

a

 Flow rates and volumes include both hot and cold water 
7 

Daily Total DHW Use for June

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Day of Month

DHW Volume (Gallons)

Fig. 8 Daily DHW volume for June 

 The normalized hourly profiles for the five DHW enduses are shown in Figs. 9 through 13. Most end-uses have a 

large enough number of events over the course of a year that 

the profiles tend to match the Benchmark hourly profiles very 

closely, even when the σfr is large, with relatively small 

differences due to random effects. End-uses with fewer events 

and large σfr (such as baths) do not align quite as well with the 

Benchmark profiles, but the correlation appears to be 

reasonable for our purpose. 

Fig. 9 Average DHW hourly profile for 

clothes washer events compared to Benchmark 

Fig. 10 Average DHW hourly profile for 

dishwasher events compared to Benchmark 

Fig. 11 Average DHW hourly profile for bath events 

compared to Benchmark 

Fig. 12 Average DHW hourly profile for shower events 

compared to Benchmark 

Fig. 13 Average DHW hourly profile for sink events 

compared to Benchmark 

Limitations 

 While the approach described in this paper has helped to 

standardize the analysis of hot water systems in advanced 

residential buildings, there are some limitations to its 

application. 

• The use of 6-min time-steps limits the usefulness of 

the event schedules for accurate predictions of 

distribution losses because unclustered sink events of 
8 

1-min or less are common, and can have a significant 

influence on the heat losses from distribution pipes. 

• For distribution systems other than the standard 

trunk-and-branch system assumed for the 

Benchmark, realistic events may be quite different 

depending on the magnitude of distribution losses, 

the presence of a recirculation loop, and the volume 

of stagnant water between individual fixtures and the 

nearest reservoir of hot water. Decreases in water 

usage but increases in energy usage are expected 

from the use of recirculation systems. 

• ENERGY STAR or other non-standard appliances 

may consume a very different amount of hot water 

for washing dishes or clothes, depending on their 

efficiencies and control strategies. 

• Day-to-day variations were designed to be consistent 

with a typical family and were not intended to 

represent differences among specific households. 

• We did not address conditional probability effects, 

such as the fact that once some number of bath or 

shower events occur (e.g., four showers for a house 

with four people), the likelihood of more drops off. 

Clothes washer events would be the opposite, 

because they tend to be clustered in real households. 

Once a clothes washer event occurs, the likelihood is 

higher that another will follow. The model treats the 

event probabilities at each time step as independent 

of the periods before and after, which is not 

completely realistic. 

 Because it is impossible to envision all the scenarios for 

advanced systems, analysts must make appropriate 

adjustments to the event schedules based on engineering 

judgment. NREL is currently developing techniques to make 

this process more convenient and consistent. 

CONCLUSIONS 

NREL has developed a series of event schedules in 6-min 

time steps for the five major residential hot water end-uses 

(sinks, showers, baths, clothes washer, and dishwasher). These 

event schedules were designed for use in conjunction with the 

Building America Benchmark, and offer the opportunity for 

significant improvements in the consistency and accuracy of 

energy analysis for certain advanced residential hot water 

systems, including recirculation loops, tankless water heaters, 

and solar hot water systems with demand-side heat 

exchangers. 

ACKNOWLEDGMENTS 

The authors express our appreciation to Ren Anderson, 

Craig Christensen, Dennis Barley, Ali Jalalzadeh, and Mark 

Eastment of NREL, along with Ed Hancock of the Mountain 

Energy Partnership for providing very constructive insights 

throughout the process of developing these event schedules. 

We also want to thank Ed Pollock and George James of DOE 

for the leadership and resources necessary to perform this 

work. 

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Department of Energy. 
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Development of Standardized Domestic Hot Water Event Schedules 

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The Building America Research Benchmark is a standard house definition created as a point of reference for tracking 

progress toward multi-year energy savings targets. As part of its development, the National Renewable Energy 

Laboratory has established a set of domestic hot water events to be used in conjunction with sub-hourly analysis of 

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