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

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

Contract No. DE-AC36-08GO28308

Strategies to Save 50% Site 

Energy in Grocery and General 

Merchandise Stores 

Adam Hirsch, Elaine Hale, and Matthew Leach

Presented at the ACEEE Summer Study 2010

Pacific Grove, California

August 15–20, 2010

Conference Paper

NREL/CP-5500-48197

March 2011
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1 

Strategies to Save 50% Site Energy in Grocery and General 

Merchandise Stores1

Adam Hirsch, Elaine Hale, and Matthew Leach, National Renewable Energy Laboratory 

ABSTRACT

This paper summarizes the methodology and main results of two recently published 

Technical Support Documents. These reports explore the feasibility of designing general 

merchandise and grocery stores that use half the energy of a minimally code-compliant building, 

as measured on a whole-building basis. 

We used an optimization algorithm to trace out a minimum cost curve and identify 

designs that satisfy the 50% energy savings goal. We started from baseline building energy use 

and progressed to more energy-efficient designs by sequentially adding energy design measures 

(EDMs).

Certain EDMs figured prominently in reaching the 50% energy savings goal for both 

building types: (1) reduced lighting power density; (2) optimized area fraction and construction 

of view glass or skylights, or both, as part of a daylighting system tuned to 46.5 fc (500 lux); (3) 

reduced infiltration with a main entrance vestibule or an envelope air barrier, or both; and (4) 

energy recovery ventilators, especially in humid and cold climates. In grocery stores, the most 

effective EDM, which was chosen for all climates, was replacing baseline medium-temperature 

refrigerated cases with high-efficiency models that have doors. 

Additional analyses presented here include specific examples of how 50% savings were 

achieved in climate zones 1A and 6A with EDM packages; a comparison of the cost 

effectiveness of different EDMs; and the generation and subsequent comparison of multiple 

solution sets that all satisfy the 50% energy savings goal. 

Background

The American Society of Heating, Refrigerating and Air-Conditioning Engineers 

(ASHRAE) is developing tools to help achieve its vision of market-viable net-zero energy 

buildings by 2030 (Jarnagin et al. 2008). To this end, ASHRAE, in conjunction with the 

American Institute of Architects, the U.S. Department of Energy, the U.S. Green Building 

Council, and Illuminating Engineering Society, has been developing Advanced Energy Design 

Guides (AEDGs) to chart technically feasible, cost-effective design paths leading to net-zero 

energy for commercial buildings. Thus far, six AEDGs have been completed, providing 

prescriptive design recommendations to reach 30% energy savings compared to buildings that 

meet the minimum requirements of ASHRAE 90.1-1999 (ASHRAE 1999a). Recommendations 

are provided for the eight ASHRAE climate zones defined for the United States. Each AEDG is 

accompanied by a Technical Support Document that describes the processes and methodologies

 1 The Alliance for Sustainable Energy, LLC, under Contract No. DE-AC36-08GO28308 with the U. S. Dept. of Energy have authored this work. 

The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States 

Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow 

others to do so, for United States Government purposes. This work was supported by the U.S. Department of Energy under Contract No. DEAC36-08-GO28308 with the National Renewable Energy Laboratory.
2 

used to develop the AEDG’s recommendations, including detailed descriptions of modeling 

assumptions and results. A set of 50% energy saving AEDGs is currently under development. 

This paper summarizes the methodology and results of the Technical Support Documents for two 

of these studies: medium-box (40,000-50,000 ft2

) general merchandise (Hale, Leach, Hirsch & 

Torcellini 2009) and grocery stores (Leach, Hale, Hirsch & Torcellini 2009). 

The following steps were used to select a mathematically optimal design for achieving 

50% whole-building site energy savings compared to a minimally code-compliant building as 

described in Appendix G of ASHRAE 90.1-2004 (ASHRAE 2004): 

o define a prototypical model by specifying design and building program features not 

addressed by ASHRAE 90.1-2004, from site and layout to miscellaneous electric loads; 

o create baseline energy models that are minimally compliant with ASHRAE 90.1-2004 for 

each of eight ASHRAE climate zones using weather information for major U.S. cities in 

those climate zones (1A-Miami, 2A-Houston, 2B-Phoenix, 3A-Atlanta, 3B-Las Vegas, 

3C-San Francisco, 4A-Baltimore, 4B-Albuquerque, 4C-Seattle, 5A-Chicago, 5B-Denver, 

6A-Minneapolis, 6B-Helena, 7-Duluth, 8-Fairbanks) from the prototype model; 

o assemble a list of energy design measures (EDMs) intended to reduce building energy 

consumption, specifying performance and cost; 

o conduct industry and peer reviews of the prototypical model and EDMs to strengthen 

initial assumptions; and 

o use Opt-E-Plus software to run an iterative sequential search optimization to select a 

combination of design features that produce 50% energy savings at the lowest cost. 

Prototype Buildings

One grocery store prototype and two general merchandise prototype buildings were 

developed. The general merchandise prototypes vary only in their plug loads. The prototype 

grocery store assumptions are drawn mainly from the 2003 CBECS study (EIA 2005) and the 

U.S. Department of Energy Benchmark Supermarket (Deru 2010). It is a one-story, 45,000-ft2

building with a 1.5 aspect ratio and 20-ft ceiling height. The façade contains 1,400 ft2 of glazing, 

giving a 27% window-to-wall ratio for that wall and 8% window-to-wall ratio overall. The walls 

are built from stucco on concrete block with rigid isocyanurate insulation (R-value varies by 

climate) and gypsum board; the roof is built up with all insulation above deck; and the floors are 

slab-on-grade 8-in. thick heavyweight concrete. Ten-ton unitary rooftop units (RTUs) with direct 

expansion coils, natural gas heating, air-cooled condensers, and constant volume fans are used to 

condition and ventilate the interior space. The grocery store contains 13 thermal zones, 

dominated by main sales (~55%), produce (17%), deli (5.4%), bakery (5%), and storage (10%). 

The remaining floor area is taken up by restrooms, meeting rooms, offices, and 

electrical/mechanical areas. Plug and process loads were taken from Deru et al. (2010), totaling 

0.884 W/ft2 and 0.384 W/ft2 for electrical and gas loads, respectively. Operating hours (6:00 a.m. 

to 10:00 p.m. 7 days/week) and occupancy were taken from ASHRAE 90.1-1989 (ASHRAE 

1989). The prototype refrigeration system is adapted from the benchmark supermarket model 

system (Deru et al. 2010), which is largely based on an example in Westphalen et al. (1996). 

There are four compressor racks: two low-temperature racks (serving frozen food cases, ice 

cream cases, and walk-in freezers), and two medium-temperature racks (serving meat cases, 

dairy/deli cases, and walk-in coolers).

The general merchandise prototypes are smaller (40,500 ft2

, 1.25 aspect ratio) than the 

grocery store prototype and have 1000 ft2 of glazing on the façade. Eighty-five percent of the 
3 

gross floor area is allocated to sales display, 8% to active storage, 1% to a meeting room, 1% to a 

dining room, 2% to restrooms, and roughly 3% in total to several other spaces such as an 

enclosed office, a mechanical room, a corridor, and a vestibule. Stores operate 7:00 a.m. to 9:00 

p.m. Monday through Friday; 7:00 a.m. to 10:00 p.m. Saturday; and 9:00 a.m. to 7:00 p.m. 

Sundays and Holidays. The two prototype models differ in their whole-building plug load 

densities: the “low plug load” model contains 0.29 W/ft2

, and the “high plug load” model 

contains 1.32 W/ft2

 (representing larger amounts of plug-in merchandise and accent lighting).

Opt-E-Plus and Sequential Search

Most EDMs were shared between the two studies, except that refrigeration equipment 

measures were limited to the grocery store analysis and plug loads were addressed in the general 

merchandise store analysis. Detailed costs and performance for each EDM were taken from the 

literature; please see the original reports for details (Hale, Leach, Hirsch & Torcellini 2009; 

Leach, Hale, Hirsch & Torcellini 2009). The EDMs that were considered are listed in Table 1.

Table 1. Categories of EDMs

Building Component Symbol Description

Infiltration IN Air barrier; vestibule

Electric Lighting LPD Lighting power density reduction

Daylighting DL Daylighting controls and skylights

Window Area WA Reduce glazing on south façade by 50%

Wall Insulation WI Eight options including baseline

Roof Insulation RI Fourteen options including baseline and four cool roof options

Fenestration Types FT Two categories, one for south façade glazing, one for skylights

Heating, Ventilation, and AirConditioningHVC Twelve options based on varying coefficient of performance, fan 

efficiency, and use of economizers

Demand Control Ventilation DCV Ventilation control based on occupancy

Energy Recovery Ventilator ERV Two effectiveness options (50% and 70%)

Frozen Food Cases FFC Six types of frozen food cases

Ice Cream Cases ICC Six types of ice cream cases

Meat Cases MC Eight types of refrigerated meat cases.

Dairy/Deli Cases DDC Five types of refrigerated dairy/deli cases.

Solar Photovoltaic Generation PV 10% efficient panels installed on 60% of net roof area (gross roof 

area-skylight area)

We used Opt-E-Plus, the National Renewable Energy Laboratory’s commercial building 

energy analysis platform, to determine combinations of EDMs that best balance two objective 

functions: net site energy savings and five-year total life cycle cost (5-TLCC). The output of the 

optimization is a 5-TLCC versus percent energy savings graph that includes one point for each 

building, and a curve that connects the minimum cost buildings for a given energy savings, 

starting at 0% savings (the baseline building) and proceeding to the building with maximum 

possible percent savings (Long, Macumber, Hirsch & Lobato 2010). 
4 

Characterization of Baseline and Low-Energy Buildings

We applied the following prescriptive requirements from ASHRAE 90.1-2004 (ASHRAE 

2004) and ASHRAE 62-1999 (ASHRAE 1999b) to construct the baseline buildings from the 

prototype models: 

o wall and roof layer thermal properties; 

o fenestration requirements for window U-value, solar heat gain coefficient, and visible 

light transmittance; 

o lighting power densities (LPDs) (whole-building weighted average LPD of 

approximately 1.5 W/ft2

 for both building types); 

o outdoor air (OA) ventilation rates, prescribed either on a cfm/person or cfm/ft2 basis, 

depending on space type; 

o minimum energy efficiency ratio of 10.1 for cooling, consistent with ASHRAE code for a 

10-ton, 4000-cfm unit; 

o heating efficiency of 80%; and 

o economizer use.

The results suggest that 50% net site energy savings can be achieved cost effectively in 

general merchandise and grocery stores, in most cases without relying on renewable energy 

generation. The following EDMs are recommended for all climate zones and store types:

o reduce LPD by 47%, and install occupancy sensors in the active storage room, 

mechanical room, restrooms, and office zones; 

o add a vestibule to the front entrance to reduce infiltration; 

o equip heating, ventilation, and air-conditioning (HVAC) RTUs with high-efficiency fans; 

o install daylighting sensors tuned to a 46.5 fc (500 lux) set point; 

o reduce south façade window-to-wall ratio by 50%; and 

o replace baseline exterior walls with better insulated constructions. 

For high plug load general merchandise stores, we also recommend reducing plug loads 

to 10% of their peak value when the store is closed. The following EDM recommendations are 

specific to grocery stores: 

o replace baseline frozen food and ice cream refrigerated cases with efficient, vertical 

models that have doors, LED lighting with occupancy sensors, and hot gas defrost; and

o replace baseline meat display cases with models that have efficient fans, anti-sweat heater 

controls, electric defrost, and sliding doors. 

Shaded overhangs were not chosen for any building type in any climate zone.

Evaporative condensers for grocery store refrigeration systems were also not chosen in any 

climate zone. Some EDMs followed climate-specific trends:

o skylights were selected in warm and hot climates where there is ample sunlight for 

daylighting; 

o high coefficient of performance (COP) (a 20% increase over baseline) HVAC RTUs were 

selected in all but the cold and marine climates, which have low cooling loads; 

o infiltration reduction measures (front entrance vestibule and envelope air barrier) were 

selected often, especially in humid and cold climates; 
5 

o economizers were generally forgone in favor of energy recovery ventilators (ERVs) 

because of the high availability of exhaust air; 

o ERVs played an important role in achieving the energy savings goal, especially in humid 

and cold climates; and 

o for grocery stores, more highly insulated opaque envelope constructions were selected in 

extreme climates (better insulated walls in hot climates and a better insulated roof in the 

coldest climate). 

The portfolio of EDMs used in the analysis is mainly limited to a familiar set of technologies. 

For example, climate-specific HVAC solutions that could yield further savings in certain regions 

were not considered.

Energy Savings by End Use 

We illustrate energy savings achieved by 50% energy saving buildings for different end 

uses using the “low plug load” prototype in two very different cases: Miami, Florida, and 

Minneapolis, Minnesota. The Miami baseline case consumes 109.3 kBtu/ft2

; the Minneapolis 

baseline case consumes 106.5 kBtu/ft2

. Savings in the low-energy prototypes exceed 50% in 

these two cases; total energy demand in the Miami low-energy case is 45.4 kBtu/ft2 (58.5% 

savings) and 47.3 kBtu/ft2 in the Minneapolis case (55.6% savings). The EDMs chosen for the 

two climate types are very similar, except that skylights are not recommended in Minneapolis 

(4% roof coverage is recommended in Miami), double-pane windows with low-e coating and 

argon fill are recommended in Minneapolis, and more wall insulation is recommended in 

Minneapolis than in Miami (R-22.6 versus R-18.1). Seventy percent effective ERV is

recommended in both locations, as is 50% reduction in façade glazing, tighter envelope and 

vestibule, 20% increased RTU COP and efficient fans, and 47% LPD reduction with occupancy 

sensors and 46.5 fc (500 lux) set point daylighting controls. 

Figure 1. EUI by End Use for Baseline and 50% Savings Cases 

The annual energy use intensity (EUI) by end use is shown in Figure 1 for the baseline 

and 50% energy savings designs. In Miami, heating energy is negligible. Likewise, exterior 
6 

lighting is very small in both locations compared to other end uses. Plug load reduction measures

were not explored in the low plug load case, so they are identical in the two models. 

Two end uses dominate baseline energy use in both climates: HVAC (heating/cooling 

plus fans) and interior lighting. HVAC energy consumption is 77% of the total in Miami and 

75.3% of the total in Minneapolis; interior lighting represents a large fraction of the remainder: 

18% of total building energy in both locations. 

Because HVAC energy is the dominant end use in heating (Minneapolis) and cooling

(Miami) dominated climates, we focus on how HVAC loads are influenced by the EDM 

selections that define the low-energy designs in Miami in August and in Minneapolis in January.

This comparison is performed using the average weekday HVAC loads for the following 

categories: people, lights, equipment, windows (heat conduction and transmitted solar), opaque 

surface conduction, infiltration, and heating/cooling of outside air. Energy expended to treat OA 

was simulated by running a second set of baseline and low-energy buildings setting OA per 

person and per square foot of building area to zero. Positive values denote transfer of heat into 

the building and negative values denote loss of energy from the building. Results are shown in 

Figures 2 and 3 for Miami and Minneapolis, respectively. In both cases, opaque heat transfer,

infiltration, and OA treatment dominate the baseline energy budget. The combination of greater 

wall insulation (to reduce opaque surface heat conduction), tighter envelope and vestibule (to 

reduce infiltration), and 70% effective ERVs (to reduce the energy and condition OA) leads to 

substantial reduction in these three terms. Other features that stand out are the dramatic decrease 

in energy for lighting in the Miami store by reducing LPD, adding daylighting controls, and 4% 

skylight coverage. The extra heat transfer through the skylights, a tradeoff with lighting energy 

savings, is represented by an increase in the “Window” category.

Analyses of diurnal opaque surface heat fluxes for an August day in Miami and a January 

day in Minneapolis are presented in Figures 4 and 5, respectively, where “Internal” refers to heat 

transfer between the air and internal mass inside the building and the line labeled “Sum” 

represents the sum of all the opaque heat transfer terms. The Minneapolis data indicate that 

energy loss through the roof represents an opportunity for additional energy savings. In Miami, 

on the other hand, heat transfer through the exterior walls is more significant than that through 

the roof, supporting the EDM selection of increased exterior wall insulation. 
7 

Figure 2. Average Weekday HVAC Loads for Miami During August

Figure 3. Average Weekday HVAC Loads for Minneapolis During January

Figure 4. Baseline Opaque Surface Heat Fluxes for an August Weekday in Miami 

 

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

1 3 5 7 9 11131517192123

W/ft2

Hour of Day

INTERNAL

WALL

FLOOR

CEILING

SUM
8 

Figure 5. Baseline Opaque Surface Heat Fluxes for a January Weekday in Minneapolis

Cost Effectiveness of Energy Design Measures

EDMs can be grouped into the following types: (1) those that reduce energy costs and 

whole building capital cost compared to the baseline alternative; (2) those that reduce annual 

EUI enough to pay back (through energy savings) increased capital cost investment over a 

particular time horizon; and (3) those that reduce annual EUI but not enough to offset capital cost 

investment over the life cycle period. In general, EDMs of type 1 are the most desirable, as they 

represent a good investment regardless of life cycle period. Type 2 EDMs are also desirable, 

though their desirability depends on the selected life cycle period; shorter life cycle periods result 

in smaller returns on investment. Type 3 EDMs are desirable from an energy efficiency 

perspective, but not from a cost perspective for the selected life cycle period, and in most cases 

would be implemented as needed to meet a particular energy savings goal.

When optimizing energy savings with respect to life cycle cost, Opt-E-Plus selects EDMs 

in approximate order from type 1 to type 3, such that type 3 EDMs, such as photovoltaics (PV), 

are selected only if needed to reach the 50% energy savings goal. Figures 6 and 7 present the 

results of a perturbation analysis for the low-energy grocery model in Miami in which selected 

EDMs were replaced with their baseline counterparts to ascertain the effects of individual EDMs 

on energy use and life cycle costs (positive values represent savings resulting from 

implementation an EDM, negative values indicate that implementation results in higher energy 

use or cost). Figure 6 presents the overall energy savings and life cycle cost savings associated 

with a select group of EDMs; Figure 7 breaks the net life cycle cost savings into capital and 

energy components such that the type can be determined. Electricity costs include a flat monthly 

fee of $33.10, total demand charge of $6.73/kW, total usage charge of $0.07/kWh, plus taxes of 

7.1%. The natural gas price was assumed to vary monthly, with an annual average cost of 

$11.65/MCF (Hale, Leach, Hirsch & Torcellini 2009). 
9 

Figure 6. Energy and Life Cycle Cost Savings by EDM for Miami Grocery

Figure 7. EDM Cost Effectiveness Breakdown for Miami Grocery

LPD reduction represents an important example of a type 1 EDM. Although the 

associated high-efficiency lighting equipment requires an increased capital investment over the 

baseline lighting equipment, it reduces the heating load enough that the HVAC system can be 

sized nearly 20% smaller than the baseline system. This reduces LPD in the overall capital 

investment with respect to the baseline building configuration. Improving the efficiency of the 

refrigerated cases is an important type 2 EDM. By making a capital investment in higher 

efficiency case models, substantial annual energy savings (at 32.5%, by far the most significant 

contribution of a single measure to the energy savings goal) can be realized, resulting in 

considerable overall life cycle cost savings. As mentioned previously, installing PV panels on the 
10 

roof is a type 3 EDM. In this case, PV was necessary to meet the 50% energy savings target, but 

its high cost of implementation resulted in a significant increase in overall life cycle cost for a 

relatively modest energy saving (2.8%). 

Diverse Paths to 50% Energy Savings

The 50% energy savings general merchandise and grocery store designs discussed thus 

far are informative, and can serve as a starting point for individual projects. However, there are 

limitations to providing just one design per location. Our cost and long-term performance data 

are uncertain enough that the designs we found to be optimal may not remain so given projectspecific data. Also building design has enough qualitative aspects that a “one size fits all” 

solution for any building type and location is unlikely. 

For these reasons, our technical reports introduce a new type of analysis for enumerating 

a diverse set of alternative designs. We run multiple optimization searches, each with a slightly 

perturbed search space. At a minimum, some EDM categories are removed from consideration 

so we can answer questions such as: “Is daylighting required to meet the 50% energy savings 

goal?” and “If I choose not to install more efficient ice cream cases, what can I do elsewhere in 

the store to meet my energy efficiency goal?” The algorithm is described in detail in an

upcoming paper by Hale and Long (2010). 

The algorithm in the full-length reports was abbreviated, in that we looked only for 

designs that forced a single design strategy to the baseline level, and did not find designs that 

excluded two or more strategies from consideration. We focus here on a rerunning of the 

algorithm for the San Francisco, California, grocery store model.

If the algorithm were run to completion, the result would be a list of all possible ways to 

reach the 50% net site energy savings goal, where different designs would be distinguished by 

the combination of design strategies set to the baseline level. Unfortunately, the significant 

computational demands (memory requirements, in particular) that would be required precluded 

an exhaustive list. The goal of design enumeration was largely accomplished, however, as 69 

feasible designs were generated. A selected number are summarized in Table 2. The strategies 

that were chosen for at least one design can be found in Table 1. 

Table 2 illustrates that in this case, there are a number of ways to reach the energy 

efficiency goal. Design 0 indicates that the most cost-effective design uses vestibules and air 

barriers to reduce infiltration, more efficient electric lights, and daylighting controls to dim the 

lights near the windows at the front of the store; however, design 73 demonstrates that, as a 

package, those changes are not required. Table 3 shows that metrics directly examined by the 

search algorithm (net site energy use and lifetime cost) vary little over the set of feasible designs. 

However, other metrics such as PV energy and peak electricity demand do vary significantly and 

can be used to inform the decision about which design best matches the goals and philosophy of 

a given project. 

In addition to generating alternative designs that reach the energy efficiency goal, 

unsuccessful searches show that a strategy is needed. For the grocery store design and the San 

Francisco run, we found that 50% net site energy savings cannot be reached with the baseline 

dairy/deli refrigerated cases. Even though PV panels cover 60% of the roof, the San Francisco 

store reached only 45.2% net site energy savings with baseline dairy/deli refrigerated cases. 
11 

Conclusions

Our analysis emphasizes the importance of reducing envelope heat transfer, infiltration, 

and energy used to condition OA to reach 50% energy savings in extreme climates and the 

critical role refrigerated cases play in grocery store energy use. We also provide a framework for 

comparing the performance and cost effectiveness of different EDMs chosen as part of a 50% 

energy savings design package. This framework facilitates better understanding of wholebuilding interactions, such as how substantial energy savings through an EDM such as LPD 

reduction can lead to capital cost savings and overall life cycle cost savings through its impact on

HVAC sizing. It also highlights where investments should be made to lower the cost of 

technologies that save substantial energy but are not currently cost effective and to develop 

reliable application packages for technologies that have the potential to save significant amounts 

of energy such as daylight harvesting. Lastly, we present an optimization framework that allows 

users to identify multiple design packages that can achieve 50% energy savings and to compare 

them using performance, life cycle cost, capital cost, and peak demand metrics. We recognize 

that using site energy rather than source energy or other energy metrics will influence the choice 

of low energy design and plan to include other metrics in future research. The results presented 

here should be used as a starting point to inform building design rather than as prescriptive 

recommendations, as the details of actual projects will differ from the idealized cost and 

performance assumptions used here.
12 

Table 2. Selected Alternative Designs for Grocery Stores in San Francisco, California*

Included Strategies**

Design 

No.

IN LPD DL WI RI FT HVC DCV ERV FFC MC Notable Attributes

0 x x x x x x x x Optimal design; lowest life 

cycle cost ($144/ft2)

1 x x x x x x x Lowest capital cost 

($127/ft2); removes 

vestibule and air barrier

7 x x x x x x x x x DCV and double paned 

skylights make up for 

removing ERV

8 x x x x x x x x x Roof insulation, double 

paned skylights, and PV 

make up for using baseline 

frozen food cases

13 x x x x x x Same as design 1 except no 

daylighting controls, slightly 

less energy savings.

59 x x x x x x x x x Lowest electric demand: 180 

kW versus 226 kW for 

design 0; significant PV (8.0 

kBtu/ft2yr)

73 x x x x x Highest electric demand: 

263 kW; no PV, baseline 

infiltration and lights

*All listed designs include reduced window area, and non-baseline HVAC systems, ice cream cases, and dairy/deli 

refrigerated cases.

** IN - infiltration; LPD - lighting power density; DL - daylighting; WI - wall insulation; RI - roof insulation; FT - 

fenestration type; HVC – HVAC; DCV – demand control ventilation; ERV – energy recovery ventilator; FFC – 

frozen food cases; MC – meat cases

Table 3. Summary Statistics for 69 Grocery Store Designs With at Least 

50% Energy Savings Over Baseline

Net Site 

Energy PV Energy

Lifetime 

Cost

Capital 

Cost

Peak 

Demand

Energy 

Savings

Statistic kBtu/(ft2yr) kBtu/(ft2yr) $/ft2 $/ft2 kW % 

Min 107.7 0.0 144 127 180 50.0

Max 115.7 8.0 162 146 263 53.4

Mean 113.8 0.5 147 130 215 50.8

σ 2.4 1.6 3.5 3.5 21 1.0

100σ/mean 0.2 27.3 0.2 0.3 9.6 2.0
13 

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Leach, M., E.T. Hale, A. Hirsch, and P.A. Torcellini. 2009. Grocery Store 50% Energy Savings 

Technical Support Document. NREL/TP-550-46101. Golden, Colo.: National Renewable 

Energy Laboratory. http://www.nrel.gov/docs/fy09osti/46101.pdf. 

Long, N., D. Macumber, A. Hirsch, and C. Lobato. 2010. Commercial Building Design 

Pathways Using Optimization Analysis. Golden, Colo.: National Renewable Energy 

Laboratory. In preparation. 

Westphalen, D., R.A. Zogg, A.F. Varone, and M.A. Foran. 1996. Energy Savings Potential for 

Commercial Refrigeration Equipment. 46230-00. Cambridge, MA: Arthur D. Little, Inc. 
F1147-E(10/2008) 

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1. REPORT DATE (DD-MM-YYYY)

March 2011

2. REPORT TYPE

Conference Paper

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4. TITLE AND SUBTITLE

Strategies To Save 50% Site Energy in Grocery and General 

Merchandise Stores

5a. CONTRACT NUMBER

DE-AC36-08GO28308

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6. AUTHOR(S)

A. Hirsch, E. Hale, and M. Leach

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NREL/CP-5500-48197

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BEC7.1203

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National Renewable Energy Laboratory

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14. ABSTRACT (Maximum 200 Words)

This paper summarizes the methodology and main results of two recently published Technical Support Documents. 

These reports explore the feasibility of designing general merchandise and grocery stores that use half the energy of 

a minimally code-compliant building, as measured on a whole-building basis. We used an optimization algorithm to 

trace out a minimum cost curve and identify designs that satisfy the 50% energy savings goal. We started from 

baseline building energy use and progressed to more energy-efficient designs by sequentially adding energy design 

measures (EDMs). Certain EDMs figured prominently in reaching the 50% energy savings goal for both building 

types: (1) reduced lighting power density; (2) optimized area fraction and construction of view glass or skylights, or 

both, as part of a daylighting system tuned to 46.5 fc (500 lux); (3) reduced infiltration with a main entrance vestibule 

or an envelope air barrier, or both; and (4) energy recovery ventilators, especially in humid and cold climates. In 

grocery stores, the most effective EDM, which was chosen for all climates, was replacing baseline mediumtemperature refrigerated cases with high-efficiency models that have doors.

15. SUBJECT TERMS

general merchandise; grocery; optimization; energy design measure; edm

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