Proposed Methodology for a Comparative Analysis of 

ASHRAEIIESNA Standard 90.1-1989 and Standard 90.1 -1 999 

Introduction 

This document details a proposed method of analysis for the U.S. Department of Energy's (DOE) 

determination of energy savings regarding BSR/ASHRAE/IESNA Standard 90.1-1 999. The 

contents ofthis document include a description of the proposed methodology to be used for the 

determination, details assumptions or proposed data sources, and elaborates on particular issues in 

doing the analysis. 

Background 

The Energy Policy and Conservation Act (EPCA) as amended by the Energy Policy Act of 1992, 

requires individual states to adopt commercial building energy standards that meet or exceed the 

energy performance of ANSI/ASHRAE/IESNA Standard 90.1-1 989. When this standard or any 

later version of ASHRAE Standard 90.1 is amended, the U.S. Department of Energy (DOE) is 

required to do a determination of energy savings comparing the amended version and previous 

version of the standard to determine if the amended version provides greater energy savings than 

the previous version. If a determination of energy savings is positive, individual states have 2 

years after publication of such a determination to certify to DOE that they have updated their 

state commercial building energy codes to meet or exceed the efficiency of the modified 

ASHRAE Standard 90.1. Section 3049b)(2)(A) of EPCA specifies that DOE has 12 months to 

make this determination fkom the time the ASHRAE Standard 90.1 is amended. 

In June 1999, ASHRAE approved BSWASHRAMESNA 90.1-1 999, a completely revised 

version of the ASHRAE Standard 90.1. Accordingly, DOE conducted a preliminary analysis of 

energy savings in office and retail buildings. These building types together represent 37.4% of 

building energy use in the U.S. That analysis compared buildings of a single defined size and 

shape, but explored variation of savings with climate, wall construction, and heating kel type. 

The analysis proposed here would provide a more detailed to look at the energy efficiency of the 

two standards across a broader range of building types and sizes characteristic of each building 

type. The analysis would provide a better representation of the improvement in commercial 

building energy efficiency between the two standards. 

Methodology Overview 

The methodology used to assess the relative energy efficiency of ASHRAE Standard 90.1 - 1989 

and ASHRAE Standard 90.1 - 1999 would be based on both a qualitative comparison of the 

 6450-01-P COMMENT 02FF
criteria in the two standards and a quantitative estimate of energy savings based on those criteria. 

Qualitative Comparison 

A qualitative comparison of the requirements in each version of ASHRAE Standard 90.1 allows 

for a comparison of the relative energy efficiency of the standard in key areas and for key 

requirements with significant impact on building energy use. The qualitative comparison allows 

the relative stringency of many sections of the standards to be quickly compared. In addition to 

the comparison of stringency, we propose to discuss some of the background behind the 

development of Standard 90.1- 1999 and how the goals set early in the development process 

influence the scope and stringency of individual requirements in Standard 90.1 - 1999. 

The proposed analysis plan provides for a qualitative comparison ofthe stringency of the two 

versions of ASHRAE Standard 90.1 in each of the following areas: 

Scope of the standard 

Building envelope requirements 

Building lighting requirements 

Building mechanical equipment requirements 

Paths to compliance 

In the scope comparison, we propose to look at the buildings and construction covered by each 

Standard. In each of the envelope, lighting, and mechanical sections, the emphasis of the 

qualitative comparison would shift to focus on the particular requirements of each section, the 

difference in coverage of building components in each section, and the relative stringency of 

comparable requirements. 

In the building envelope section, the comparison would focus on the impact of the different 

building envelope requirements on the building heating and cooling loads for different building 

types and climates. The envelope comparison would examine requirements for all envelope 

components, including roofs, walls, floors, and fenestration as well as explore variations in 

requirements by envelope construction type and window-to-wall ratio. 

In the building lighting section comparison, the focus would be primarily on the impact the 

different lighting requirements have on lighting energy use. The comparison would look 

separately at the whole building and space-by-space lighting requirements in both standards in a 

variety of commercial building types, as well as examine the effect of any additional lighting 

power allowances in the two Standards. 

The building mechanical section comparison would be broken down into comparisons of 

equipment efficiency requirements and system design requirements. Comparison of the equipment 

efficiency requirements would be relatively straightforward between both standards, although 

there would be changes in equipment covered. The system design requirements affect both the 

system efficiency as well as thermal loads, and in addition may have direct energy impacts due. for 

 6450-01-P COMMENT 02FF
instance, to fan system requirements. Relative stringency and estimated impact on building 

efficiency would be prepared based on practical application of the system design requirements in 

each standard. Variations in coverage of equipment and systems would be examined for both 

standards. 

Each standard may have multiple paths for compliance. In general, we propose to enumerate 

multiple paths to compliance where they exist, but would not go into great detail comparing the 

relative stringency of alternate paths internal to a single code or between codes. Rather we 

propose to focus on what we believe is the most common approach to using the standards or 

codes in question. 

Quantitative comparison 

The purpose of the quantitative analysis is to provide DOE an evaluation ofthe relative energy 

efficiency of the two versions of ASHRAE Standard 90.1 when taken as a whole, as well as an 

estimate of the energy impact of the change in standards on commercial building energy 

efficiency. The quantitative comparison of energy codes would be based on whole building energy 

simulation of buildings built to either the ASHRAE: Standard 90.1-1989 or 90.1-1999. It is not 

feasible to simulate all possible permutations of building design, nor is the data available to 

correctly weight each possible permutation in each possible U.S. climate as a &action of the 

national building construction mix. Hence, the quantitative analysis focuses on development of a 

set of assumptions that characterize a particular subset of the building population, simulation of 

buildings that represent that subset, and provision for aggregation of the energy savings on a 

national level. Where there is concern about aggregation weighting, limited sensitivity analyses 

would be performed to explore the variation of energy performance within a population subset. 

It is recognized that there would be specific requirements of the standard that would not be 

amenable to simulation within the scope of this analysis. This is particularly true of system design 

issues in the mechanical sections ofthe standards. For most of these specific requirements, any 

differences in requirements would suggest an obvious stringency change between the standards, 

and this would be explored in the qualitative analysis. 

Simulation Methodology 

The simulated buildings would utilize a 15 zone building prototype developed for the Department 

by Pacific Northwest National Laboratory (PNNL). The energy use &om the simulations would 

be scaled to correctly reflect energy use in the mix of characteristic building sizes and shapes for 

each representative building type. Data on building size and shape would come &om the 

Commercial Building Energy Consumption Survey (CBECS) Database (EIA 1998, EIA 1994). 

The simulations would be performed on seven representative building types in 11 representative 

U.S. climates. 

Energy Use Intensities (EUIs) developed for each zone in each representative building would be 

weighted by total national square footage in each representative building category to provide an 

 6450-01-P COMMENT 02FF
estimate of the national energy savings (Friedrich 1995). Note that only changes to new buildings 

would be considered in the quantitative analysis; the scope of ASHRAE Standard 90.1-1999 also 

addresses additions and renovations to existing buildings. While there may be significant energy 

savings fiom applying an energy standard to building renovations, it would be extremely difficult 

to quantifL this impact. This proposal would only examine the impact of ASHRAE Standard 

90.1-1 999 on building renovations in the qualitative comparison of the two standards. 

The quantitative analysis is broken into five steps: 

1) Describe buildings and climates for analysis 

2) Develop building models with characteristics for each standard level 

3) Simulate building prototypes in each climate 

4) Scale energy use fiom simulated buildings to all buildings of representative type 

5) Aggregate energy use across building categories to national energy use estimate 

In step (I), the buildings types and climate zones for analysis would be developed. We propose to 

use seven representative building types which together represent approximately 80% of 

commercial building energy use in the U.S. (EIA 1998) A description of commercial building 

energy use and the choice behind these seven representative buildings is discussed in a later 

section of this plan. The buildings would be simulated in 11 U.S. climates. These 1 1 climates 

were chosen based on a statistical clustering analysis of 11 distinct climate parameters important 

to building energy use. The climate data set was the 232 Typical Meteorological Year (TMY) 

hourly weather files compiled by the National Oceanographic and Atmospheric Association 

(NOAA). A brief description of the development of these 1 1 representative climates is discussed 

in Appendix A of this document. In addition, in step (1) the proposed base 3-story model, 15 

zone building model is described and the proposed characteristic size, systems, and equipment and 

load schedules are developed. 

In step (2), the building envelope, equipment efficiency, and lighting levels ofthe prototype 

building models would be given values characteristic of either ASHRAE Standard 90.1-1989 or 

Standard 90.1-1 999. 

In step (3), each building prototype is simulated using the Building Loads and System 

Thermodynamics (BLAST) software. The annual energy use for each zone in the 15 zone model 

is extracted fiom the results. 

In step (4), the zone loads fiom simulation of the prototype buildings would be mapped to 

corresponding areas in the population of U.S. buildings. To estimate the national impacts, we (a) 

map the climate locations onto regions, (b) estimate the weights each of the 15 prototype building 

zones would have in a typical mix of buildings of that representative type in each geographic 

region, (c) estimate the weight each building type has in each geographic region, and (d) estimate 

the fiaction of each year's new commercial construction represented by the type and construction 

of each simulated building within 11 separate regions of the US.. The estimate of new building 

growth in each region would come from EIA valuations of commercial construction in each 

 6450-01-P COMMENT 02FF
census division as modified by relative construction cost estimators for locations in each division. 

We propose to use MEANS (MEANS 1998) as a source for relative construction cost estimates 

by region. The estimated weights for the prototype zones would be based on the assumption of a 

typical perimeter depth for buildings equivalent to that in the prototype model (15 feet). This 

would ensure that the relative proportion ofthe energy use due to loads that scale with the floor 

area (such as ventilation loads) to loads that scale with the wall area (e.g. window loads) would 

be constant between the prototypes and the actual building populations. 

In step (9, an estimate of the total national impact on energy use of going fiom Standard 90.1- 

1989 to Standard 90.1 - 1999 would be made by weighting the zonal EUIs for each prototype 

simulated by the estimated square footage of each building zone in a national mix of buildings. 

Selection of the Representative Building Types and Climate Locations for 

the BLAST simulation 

Based on the review of the annual energy use of principal building types surveyed by the 1995 

CBECS, a ranking of the building types by energy usage was developed and is shown in Table 1. 

Seven of the top eight building types (in terms of the magnitude of annual energy use) account for 

78.4% of the cumulative total energy consumption of all commercial buildings. Since they account 

for more than three-quarters ofthe total commercial building energy consumption, the seven 

building types listed in bold type in Table 1 were selected as the representative building hes that 

would be simulated using the BLAST software. 

Table 1. Energy Consumption by Principal Building Activity (trillion Btu) 

Principal Building 

Activity' 

Oftice 

Mercantile and Service 

Education 

Health Care 

Lodging 

Annual Energy 

Us2 

Public Assembly 

Food Service 

Warehouse and Storage 

I Public Order and Safetv 1 124 1 2.3% / 97.1% I 

1,019 

973 

614 

56 1 

461 

Other 

Percent of Total 

449 

332 

325 

173 1 3.3% 1 92.2% 

1 Total 5,323 / 100.0% 1 

The building activities in bold are being explicitly considered for the Determination analysis. 

Source: CBECS 1995 

Cumulative 

19.1% 

18.3% 

11.5% 

10.5% 

8.7% 

Religious Worship 

Vacant 

19.1% 

37.4% 

49.0% 

59.5% 

, 68.2% 

8.4% 

6.2% 

6.1% 

Food Sales 

104 1 2.0% 1 99.0% 

51 1 1.0% 1 100.0% 

76.6% 

82.8% 

88.9% 

137 1 2.6% 1 94.8% 

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CBECS characterizes Health Care facilities as either inpatient or outpatient care. The former 

facilities are difficult to characterize in terms of typical internal or ventilation loads, although a 

general trend is 24 hour operation in at least some portion of the building. The latter facilities are 

generally similar to office buildings in terms of loads and hours of operation. It is proposed 

decided that the outpatient care/doctor's office segment of the Health Care building type be added 

to the mix of buildings using the "office" building model to represent space cooling, heating and 

water heating loads. This would bring the percentage of the energy consumption captured in this 

analysis to approximately 8 1%. 

The national climate variations for the BLAST simulation would be represented by the same 

eleven climate locations used in the ASH= Standard 90.1-1999 development work, with the 

exception that TM2 rather than TMY weather tapes would be used. A TMY2 weather tape is 

available for all of the original locations except Orlando, Florida. For this analysis, Tampa, 

Florida would be chosen as the closest match to the original Orlando climate. The eleven climate 

locations we propose to use are: 

Providence, Rhode Island Detroit, Michigan Minneapolis, Minnesota 

Knoxville, Tennessee Shreveport, Louisiana Tampa, Florida 

Denver, Colorado Phoenix, Arizona Seattle, Washington 

Fresno, California Los Angeles, California 

These original I I locations and the areas of the country they represent are shown in Figure 1. 

For this analysis, a single 15-zone prototype would be used as the basis for all of the modeled 

buildings. Justification for using this prototype is discussed in (Barwig 1996) and (Friedrich 

1995). The 15-zone prototype would provide for a zone with each possible exposure along a 

cardinal axis. For each building type actually modeled (e.g., office buildings), the internal loads, 

ventilation loads, and operation schedules would be modified to represent those characteristic of 

that particular representative building type. The 15-zone building model would be used to 

estimate the annual energy use intensity for each of several different end-uses (lights, plug loads, 

hot water loads, cooling, heating, and fan power) in each building zone. The annual loads for 

each of the 15 zones in the simulated model would then be scaled to represent the relative 

proportion of each of these zones in a national mix of buildings of that same building type, with 

the same representative climate, and with the particular characteristics modeled. 

Base Building Assumptions: 

The preliminary office and retail building simulations were based on a 3-story, 48,000 ft2 building 

prototype, with each floor consisting of a 10,000 ft2 core zone and four 1,500 ft2 perimeter zones. 

We propose to continue to use this prototype. The building would have a variable window-towall ratio and would assume that windows are distributed evenly across each floor and on all sides 

of the building. Each floor of the prototype building would be 13 feet high. The top floor of the 

building would have a separately zoned 4-foot high plenum above a 9-foot high office space. 

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Figure 1 - US Map Showing Eleven TMY Climate Locations (cities) and Corresponding Climate 

Regions (dark boundaries) and Nine Census Divisions (shaded areas) 

Space cooling requirements in the mode1 would be met using 15 separate single-zone spaceconditioning systems. All building types with the exception of warehouse would be assumed to 

be heated to 70°F and cooled to 75 OF during primary occupied periods. During setback periods, 

these buildings would be assumed to be heated to 55 OF and cooled to 85 OF. The present 

proposal is to simulate warehouses as primarily belonging in the "semi-heated" building category 

defined in ASHRAE Standard 90.1-1999, with heating set point temperatures at 55"F, and no 

cooling. DOE is requesting stakeholder input on the fraction of new warehouse construction that 

would actually fall into this category. If it is deemed that a large fraction of warehouse 

construction would not fall into the semi-heated category, DOE may split this into two categories. 

Equipment schedules and loads have been taken from the preliminary findings report developed 

for the EPACT standards program (Barwig 1996). These loads and schedules represent input 

from a variety of sources, but are heavily based on three data sources: ASHRAE 90.1 - 1989s 

whole building tradeoff method, metered commercial building loads data, and CBECS reported 

building schedule data. Ventilation rates for the simulated buildings would be designed to meet 

ASHRAE Standard 62-1989 requirements for both versions of ASHRAE Standard 90.1. 

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Envelope Building Parameters 

Windows 

An average window-to-wall ratio (WWR) for each building type would be extracted out of the 

CBECS 92 data set for each building type and census region. While not completed at this time, 

previous evaluations of the CBECS data set have suggested that for many building types, the 

window-to-wall ratio may vary substantially from the average value. Because of the significance 

of this parameter, it is proposed to use the average value in the quantitative analysis; however, a 

sensitivity analysis would be performed varying the \NWR from a low of 10% to a high of 40% in 

two non-residential building type (office and education) to explore the impact of this variation on 

the energy performance of the two standards for each of the 1 1 climate locations. Fenestration 

Requirements for ASHRAE Standard 90.1-1989 (U-value and Shading Coefficient (SC)) would 

be developed from the ENVSTD 2.0 envelope tradeoff software supplied with ASHRAE 

Standard 90.1 - 1989. Fenestration Requirements for ASHRAE Standard 90.1 - 1999 (U-value and 

Solar Heat Gain Coefficient) would be extracted from the tables in Appendix B of that standard. 

Opaque WaIIs and Roofs 

It is proposed to separately model each building type using either a mass wall construction or 

lightweight wall construction. The ENVSTD 2.0 tradeoff software would be used to estimate 

wall and roof U-value requirements for ASHRAE Standard 90.1-1989 for each building type. For 

ASHRAE Standard 90.1-1999, the opaque wall and roof requirements would be taken from 

Appendix B ofthat standard. Metal-frame wall construction would be assumed as the basis for 

lightweight walls for buildings built under ASHRAE Standard 90.1 - 1999. Metal-frame wall 

construction is believed to be the most common type of lightweight wall construction currently in 

use for commercial buildings. 

Table 2 shows the wall U-values Standard 90.1-1999 requires for non-residential construction in 

the proposed 1 1 climate locations. Heating degree days referenced to 50°F and 65 OF base 

temperatures are also shown. For seven ofthe eleven climates, maximum U-value requirements 

for metal-fiame walls are higher than for wood fiame or metal building construction.. However, 

for colder climates, maximum U-value requirements for metal-frame walls are lower than the 

other two construction types. Wood frame wall constructions are approximately equivalent to 

metal fiame for the coldest climates, but are lower for the warmer climates 

We are actively looking for data addressing the relative frequency of the lightweight wall types for 

construction in each building type and census region. We welcome any input on wall construction 

practices for each building type and region. 

For roofs under ASHRAE Standard 90.1 - 1999, we propose to use U-value requirements for the 

"insulation entirely above deck" roof category. This category reflects the requirements for a 

typical built-up roof construction. CBECS construction data suggests that built up roofs are the 

most common construction type in commercial buildings. Table 3 shows the maximum U-value 

 6450-01-P COMMENT 02FF
allowed for each roof type in 90.1-1999. For 10 of the 11 locations, the "insulation above" deck 

U-value requirement is approximately 2.5% less than that of the metal building U-value 

requirement. In the remaining climate, Los Angeles, the "insulation above deck" maximum Uvalue requirement is significantly higher than that allowed for metal buildings; however, the total 

amount of heating is expected to be low for that climate and the national impact small. The 

maximum U-value requirements for the "attic and other" category are substantially less than the 

other roof type. It is recognized that not modeling roofs with attics would introduce some bias 

Table 2. Non-Residential Lightweight Wall U-values in 90.1-1 999 

1 I Metal ( Wood I Metal 1[ 

Table 3. Non-Residential Roof U-value Requirements in 90.1 - 1999 

Bin 

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toward underestimating the energy savings with the 90.1-1999 standard for buildings which do 

use this roof type. 

We are seeking fkrther input about the relative kequency of the roof construction types for each 

building design. There would be no attempt to explore the solar absorptivity/U-value tradeoff in 

ASHRAE Standard 90.1-1 999 in the quantitative analysis, primarily due to the difficulty of 

determining the frequency of use of this tradeoff in fkture construction as well as the technical 

difficulty of correctly evaluating the impact of the tradeoff on different roof constructions. 

Floors 

Floors would be modeled as slab on grade construction for all buildings, and differences in slab 

insulation requirements between the standards would not be modeled. None of the whole building 

simulation software available are believed to adequately model ground-heat transfer effects, and 

no attempt would be made to model them for this exercise. It is proposed that the relative 

stringency of slablfloor insulation requirements be examined only in the qualitative analysis. 

Lighting Loads 

We propose to use whole building lighting power density (LPD) values for the quantitative 

analysis portion of the Determination. A quantitative analysis of the lighting requirements in the 

ASHRAE/IESNA Standard 90.1-1999 could, in theory, be made using either the "whole building" 

or "space-by-space" compliance methods. Because of the complexities of the space-by-space 

method and the fact that it relies on specific building characteristics that are unknown in both 

standards, the whole building method is proposed here as the only practical method for a 

comparison across a wide variety of new building construction. In addition, it is believed that the 

whole building approach is likely the most common method used for establishing compliance with 

either standard, as it essentially entails only a count of fixtures and wattage for the entire building. 

The whole building LPDs in ASHRAE Standard 90.1-1999 were derived to include all lighting 

power for typical designed buildings. The corresponding UPD values in ASHRAE Standard 

90.1989 are also meant to include all building lighting but are presented in a set of building size 

ranges. With the exception of choosing an appropriate building size range for the Standard 90.1 

1989 whole building requirement based on average or typical building sizes, the whole building 

values in both standards offer a simple, straightforward, and reasonable comparison basis. 

The space-by-space values in the 1989 and 1999 standards are generally equitable in terms of their 

inclusion of all space lighting. However, there are two major differences between the two 

Standards: 

1. The ASHRAE Standard 90.1-1 989 retail space LPDs include potential merchandise display 

lighting. The ASHRAE Standard 90.1-1 999 values do not - in Standard 90.1 - 1999, fine 

 6450-01-P COMMENT 02FF
merchandise display lighting is allowed as an added power density when it is needed. Similarly, 

decorative (chandeliers, etc.) and highlight (art, etc.) lighting, as well as additional lighting 

power for video display terminal (VDT) office lighting are treated as specific additional 

allowances in Standard 90.1-1 999 and can be used only for the specified luminaires or 

application (i.e. not the whole space). This makes the LPD values for these building spaces 

incomparable. Determining what fiaction of the Standard 90.1 - 1999 additional lighting 

allowance for these space functions would exist in the marketplace is not possible with the 

available data. 

2. The Standard 90.1999 LPDs incorporate the effects of expected typical room geometry on the 

effectiveness of lighting fixtures which effects the required LPD value. This is done by 

incorporating into the calculation process a lighting Room Cavity Ratio (RCR) determined to 

be appropriate for each space by professional consensus. Standard 90.1 - 1989 accounts for 

room geometry effects by applying an Area Factor (AF) to each space's LPD value. The AF is 

a fknction of the room geometry. Because the actual room geometry and therefore the exact 

AFs are not known for the set of real buildings in the marketplace, it is not possible to make 

use of space-by-space values without assuming each spaces characteristics. 

These primary differences in the application of space-by-space values in standards compliance 

makes any comparisons using them difficult and open to much interpretation. Using the whole 

building allowance avoids the need to guess or negotiate "typical" values for the fraction of the 

additional lighting power allowance that would exist on average across a building type, or the 

average or typical room geometry inside buildings. 

We propose to do a qualitative comparison of lighting requirements for specific space types as 

shown in the two standards by converting the Uniform Power Densities (UPDs) in ASHM 

Standard 90.1 - 1989 to directly comparable Lighting Power Densities (LPDs) using room geometry 

factors that are effectively those used for ASHRAE Standard 90.1 - 1999 development. This would 

allow comparison ofthe lighting power densities for the same set of spaces the 90.1 lighting 

subcommittee used in the development of ASHRAE Standard 90.1-1999. The qualitative analysis 

would also discuss the relative impact of the "additional lighting power allowances" for specific 

applications. It is proposed to examine this by calculating the amount of floor space that would 

have to be given an additional lighting power allowance for to result in a higher lighting power 

density than what is allowed under ASHRAE Standard 90.1 - 1989 for the 90.1 - 1989 retail space 

categories assumed in the lighting section of that standard. The fiaction of areas for the Standard 

90.1-1999 allowances would be would be compared to estimates by lighting designers and others 

of what would be characteristic usage areas for these retail space types. 

 6450-01-P COMMENT 02FF
Mechanical Equipment 

As discussed, the heating and cooling loads in the simulation models would be assumed met using 

single zone cooling and heating equipment for each building type (packaged DX systems and either 

gas or electric furnaces). The performance of each system would be established as follows: 

Cooling System Efficiency: 

The improvements in cooling equipment efficiency would not be equal across all equipment types 

or equipment size categories, nor would the baseline cooling efficiency be constant. One cannot 

identi@ the characteristic efficiency improvement for all cooling equipment except through a 

weighting process across equipment types and size ranges. To assess the impact of changes in 

equipment efficiency across the many categories of packaged cooling equipment, the equipment 

efficiency modeled for each standard would be represented by an estimated load-weighted average 

equipment efficiency based on a combination of shipment volume weighting and anticipated load 

for each piece of equipment. The product of the shipment volume, a characteristic equipment 

capacity, and the characteristic full load equivalent operating hours for each piece of equipment 

would be used to provide a weighting factor for the equipment efficiency. The anticipated load 

would be based on equivalent full load operating hours that have been established for each of the 

seven building types through recent work in DOE'S EPACT Equipment Standards Program. 

Cooling equipment shipment data has been made available to that program from Air Conditioning 

and Refrigeration Institute (AN) for key pieces of EPACT covered equipment. Requests would 

be made to supplement that data for larger non-EPACT covered equipment. 

We propose one significant change fiom the preliminary comparison done in June 1999. For this 

comparison, it is proposed to include 3-phase (commercial) air-cooled cooling equipment less than 

65,000 Btulh cooling capacity in the estimate of average equipment efficiencies. The equipment 

efficiencies for ASH= Standard 90.1-1999 are identical to that in ASHRAE Standard 90.1- 

1989. 

We propose to use BLAST to calculate supply fan energy usage separate fiom compressor/ 

condenser energy usage by supplying the hnlmotor efficiency and static pressure. We would then 

have BLAST calculate fan power using the airflow rate fiom the system design calculations. The 

compressor energy usage at design conditions would be characterized by the "design full load 

power ratio" (DFPLR) which we would enter as a user input. The DFPLR is essentially the same 

as the Energy Input Ratio used in DOE2.1, and is calculated as: 

DFPLR = (1 -R) / (EERf3.4 13 + R) 

where R is the ratio of supply fan power to full load power at nominal design test conditions. 

Based on a review of existing products, R has been assumed equal to 0.10 for equipment meeting 

the nominal efficiency requirements specified in the standard. 

 6450-01-P COMMENT 02FF
Gas Furnaces: 

Space heating equipment efficiency would be modeled using an estimated thermal efficiency for the 

furnace. Standard 90.1 specifies minimum combustion rather than thermal efficiency requirements. 

To convert between combustion and thermal efficiency for this equipment type, a 1.5% rated shell 

loss rate would be used for furnaces under ASH= Standard 90.1-1 989. This value represents 

the estimated shell loss rate for commercial furnaces assumed in the Standard 90.1 - 1999 

equipment efficiency analysis as typical for gas furnaces. For the Standard 90.1-1999 simulations, 

a 0.75% shell loss rate would be used as is specified under 6.2.1 of ASHRAE: Standard 90.1-1999. 

Note that these loss rates are measured under laboratory conditions. There would be a shell loss 

multiplier of 3.3 that is applied to the rated shell loss to reflect shell loss under typical outdoor 

conditions (see U.S. Code of Federal Regulations 10CFR430 Subpart B Appendix N). The 

thermal efficiency for furnaces would thus be calculated as: 

Thermal Efficiency = Combustion Efficiency - Shell Loss Rate x 3.3 

The 15-zone building model we propose to use is specifically designed to have core and perimeter 

zones with defined exposures (e.g., top floor, north perimeter). By proper scaling and summation 

of the loads for all 15 zones, it is possible to use the results fiom the 15-zone model to mimic the 

energy use for buildings with different form factors or number of stories. Because the energy 

usage in each zone is designed to be representative of energy use with arbitrary sizes of equipment, 

we would make no attempt to model pilot energy use in the gas space heating equipment. Pilot 

energy use is a function of the number of individual furnaces used in the building and thus is 

strongly dependent on how the building is zoned in real life. The simulator must also decide if gas 

would be supplied to these furnaces after the heating season is over. Since ASHRAE Standard 

90.1 - 1989 allows the use of a standing pilot and ASHRAE Standard 90.1 - 1999 requirements do 

not allow the use of a standing pilot light, not assuming a pilot for ASHRAE Standard 90.1-1 989 

standard would underestimate this increase in stringency of the standard, however it is believed 

that in the real world, the use of pilot lights has become less common on this type of heating 

equipment. It is noted that the relatively small increase in thermal efficiency for rooftop gas 

furnaces in ASHRAE Standard 90.1 - 1999(-2%) is approximately the same as that expected for 

unit heaters, which are more commonly used equipment type for warehouses. 

Electric Resistance Furnaces 

Buildings heated with electric resistance heat would be assumed to use forced air distribution 

systems. For the purpose of this analysis, the casing loss assumptions for electric furnaces would 

be assumed equal to that for gas furnaces. This is a modiJication from what was done for the 

preliminaiy comparison of of$ce and retail buildings presented in June 1999. 

Water Heating Effiency 

Typical water heating efficiencies would be based on a shipment weighted average of efficiency, 

capacity, and full load equivalent operating hours as recently established by the commercial 

equipment standards program for each water heater type. Typical standby loss levels would be 

based on a similar shipment weighted standby loss using the same characteristic tank sizes for each 

equipment category as were used in the development of ASHRAE Standard 90.1 - 1999. The 

characteristic standby loss rate would then be normalized to the characteristic input rating. 

 6450-01-P COMMENT 02FF
Relative peak to average water heating loads for the proposed building types would be based on 

data fEom the ASHRAE Applications Handbook (ASHRAE 1995) where a one-to-one 

correspondence can be established between the handbook data and the proposed building types. 

Simplified profiles which provide the average and peak service water heater loads for each building 

category, in conjunction with the normalized average standby loss values, would be used to 

estimate the average water heating energy use per square foot for each building category being 

, analyzed for each standard. This approach mirrors that used by the DOE'S commercial equipment 

standards program in its recent review of the proposed ASHRAE standards for commercially sized 

service water heating equipment. 

For the purpose of this analysis, it would be assumed that buildings that use electric resistance for 

space heat would use electric resistance for heating service hot water, and that buildings that use 

gas space heat would also use gas for heating service hot water. 

Fan Power 

Both Standard 90.1-1989 and Standard 90.1-1 999 regulate fan power through a fan system 

efficiency metric. Standard 90.1 - 1989 specifies a maximum fan motor wattage (measured) per cfin 

of designed air volume in the system. Standard 90.1-1999 by contrast, specifies a maximum motor 

nameplate horsepower per cfin of design air volume. The purpose of the change to nameplate 

horsepower was to provide for easier inspection and compliance verification. The 90.1 - 1999 

requirements were developed to provide equivalent stringency with the 90.1-1 989 requirements, 

but include proper allowance for motor and drive eficiency, as well as provide a proper allowance 

in motor sizing for the discrete steps of motor horsepower available. For example, a designer may 

create a building fan system design that requires a calculated 2.8 brake horsepower/cfin to move a 

nominal 3000 cfin at the requisite static pressure. Since the next standard motor size available 

would be 3 hp, it is likely that a 3 hp motor would be purchased for the design whether the 

requirement is in wlcfin or motor nameplate hplcfin; we would therefore assume effectively no 

change in the actual fan energy use of the system. 

Additionally, discussion with system designers during the development of 90.1-1999 indicated that 

the fan power limits in both versions of 90.1 are higher than would be seen in many typical 

installations. Both the maximum wattage or nameplate horsepower requirements are set 

conservatively high to allow for instances of longer than average duct runs or constraints in duct 

area. Modeling the maximum fan power allowances instead oftypical fan power puts more fan 

heat into the system and incorrectly shifts the heating and cooling loads balance in the simulated 

building. 

For the purpose of this comparison, we would assume that there is no net change in base fan 

power requirements between both versions of Standard 90.1. However, there may be changes due 

to incorporation of system components. We propose to model each building assuming a base fan 

static for the system with adders for particular buildings and system design as shown in Table 4. 

The increase in fan static for economizers is based on data collected by the 90.1 mechanical 

committee. The increase in fan static for packaged equipment with gas hrnaces sections is 

calculated based on the 0.2 EER deduction allowed for packaged systems with a gas hrnace 

heating section (approximately 0.2 in fan static), assuming a constant compressor performance 

 6450-01-P COMMENT 02FF
(DFLPR) for systems with and without a gas heating section. Thus, for an office building using gas 

hrnaces and economizers, the total fan static would be 2.50 in. H,O 

Economizers 

We propose to model building systems incorporating economizers and not incorporating 

economizers in each climate. The economizer requirements in both standards are functions of 

system size and of climate. In order to account for economizer usage, we propose to weight the 

EUIs for the economizer and no-economizer simulations using appropriate weights for each 

climate. 

Table 4. Fan Static Assumptions 

Base System Fan Static Building Type Adders System Design Adders 

a (in. H20) (in. H20) 

Aggregation to National Energy Savings 

The simulations described previously would result in 616 total building permutations for the 

quantitative analysis, broken out as follows: 

I 1 climates 

7 building types 

2 wall types (lightweight or frame) 

2 economizer scenarios 

2 heating types (gas or electric resistance heat) 

The permutations would be aggregated to a national level through the following steps: 

I. Extract zone EUI data from all simulations 

2. Convert zone EUI data to separate perimeter and core EUI data for each building floor 

3. Weight EUI data to account for economizer usage in each climate 

4. Map resulting climate-based EUI data to sub-census division EUI data 

5. Weight sub census division core, perimeter, and floor-type EUI data by corresponding 

building areas in each census divisions building population 

6. Weight resulting whole building EUI data by wall-type and heating-type weights 

established at census region level 

7. Weight resulting sub census division representative building EUI data by relative areas of 

each representative-building type in each census division 

8. Weight resulting sub census division EUI data by estimated new commercial building 

construction established for each sub census division 

 6450-01-P COMMENT 02FF
Extraction of Zone EUI data from Simulated Buildings. 

A preliminary step in the aggregation process would be to extract average energy use intensity 

(EUI) data for the perimeter and core areas on each floor of each of the simulated buildings. EUIs 

would be extracted separately for space cooling, space heating, ventilation, lights, plug loads, and 

service hot water energy use. These EUIs would be aggregated by he1 type for each simulated 

building. 

Conversion of Zone EUI data to CorelPerimeterlFloor-level EUI data 

Each floor of the simulated building would have 4 perimeter zones. The average perimeter zone 

EUIs would be calculated for each floor of the 15 zone model, translating the zone-level EUI data 

for each of the 15 zones to average EUI data for 6 perimeterlcore and floor level combinations. 

Upper floor, perimeter zone Upper floor core zone 

Midlevel floor, perimeter zone Midlevel floor, core zone 

Bottom floor, perimeter zone Bottom floor, core zone 

Since CBECS or other known data sources do not capture information about the orientation of 

buildings, particularly the orientation of high versus low fenestration areas, the underlying 

assumption we propose to use is that buildings in the general population are randomly oriented. 

This assumption is also implicit in the description of the proposed 15 zone building model in that 

the model would assume a square building with equivalent envelope construction and window-towall ratio on all sides. 

Weighting of Economizer and No Economizer Results 

The corelperimeterlfloor-level EUIs fiom the economizer and no-economizer simulations in each 

location would be weighted by an estimate ofthe fiaction of cooling equipment for which an 

economizer would be required in each climate. The mix of packaged cooling equipment in each 

census division would be assumed identical to the national mix of packaged equipment and would 

be based on data provided by ARI. For each of the I I locations, we propose to estimate the total 

shipped capacity of cooling equipment for which an economizer would likely be required and for 

which an economizer would not be required under each standard using the national mix of 

equipment shipments. For each climate, the fiaction of shipped cooling capacity requiring 

economizers would be used to weight the cooling energy EUIs fiom the economizer and no 

economizer simulations for otherwise identical buildings. 

 6450-01-P COMMENT 02FF
Mapping between Results by Climate Location and Results by Census 

Division 

The next step in the process would be to map the results for building simulations fiom the climate 

locations to census data. This intermediate step would be necessary to support aggregation across 

building types, since demographic information on existing building square footage would be 

available by census division. Therefore, the climate location results must be translated to a census 

division basis to incorporate demographic data for the various building types in the analysis. 

This step would be used to determine relative influence of multiple climate-location results within 

each census division For example, the Pacific census division is composed of three states: 

Washington, Oregon, and California. This census division would contain all or part of five climate 

locations each represented by a typical meteorological year (TMY2) city (and corresponding 

weather tape): Denver, Seattle~Tacoma, Phoenix, Los Angeles, and Fresno. We would then 

determine the relative influence of these five cities on the Pacific census division results. To do so, 

we propose to make use of data developed for the commercial equipment standards program 

which has defined a weighting matrix for eleven sub census division as shown in Table 5. 

Table 5 Aggregation Weights for Eleven Sub Census Divisions 

This matrix was developed based on population data for metropolitan areas falling within 

geographical regions corresponding to each climate location shown in Figure 1. The census 

definition of the Mountain and Pacific census divisions are problematic in that each separately 

contain two sub-regions that are have very wide disparities of climate and electricity prices. 

Generally, the warmer areas within these regions - California and the desert Southwest - have 

much higher electricity prices than the northern areas. Thus, the continued use of the conventional 

census divisions would blur the relative economic impact that would result in these areas %om the 

adoption of new building codes. To resolve this, these divisions were separated 

into sub-division level. The cleanest case is that for the Pacific census division, where a state 

Providence 

Detroit 

Minneapolis 

Knoxville 

Shreveport 

Tampa 

Denver 

Phoenix 

Seattle 

Fresno 

Los Angeles 

Sum 

New 

England 

1 000 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

1000 

MidAtlantic 

1 000 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

1000 

East 

North 

Central 

0 

0 992 

0007 

0 

0 

0 

0 

0 

0 

0 

0 

1000 

East 

South 

Central 

0 

0 

0 

0675 

0326 

0 

0 

0 

0 

0 

0 

1000 

Mountain 

South 

0 

0 

0 

0 

0 

0 

0 

1 000 

0 

0 

0 

1 000 

West 

South 

Central 

0 

0 

0 

0134 

0806 

0 060 

0 

0 

0 

0 

0 

1000 

West 

North 

Central 

0 

0 600 

0400 

0 

0 

0 

0 

0 

0 

0 

0 

1000 

Mountain 

North 

0 

0 

0 

0 

0 

0 

1000 

0 

0 

0 

0 

1000 

South 

Atlantic 

0 

0 

0 

0507 

0177 

0316 

0 

0 

0 

0 

0 

1000 

Pacific 

South 

0 

0 

0 

0 

0 

0 

0005 

0 

0 

0157 

0 838 

1 000 

Pacific 

North 

0 

0 

0 

0 

0 

0 

0117 

0 

0 884 

0 

0 

1000 

 6450-01-P COMMENT 02FF
level dis-aggregation separates Oregon and Washington ("'Pacific North") fiom California 

("Pacific South"). In the Mountain division, the break is represented by separation into a 

"Mountain North" region, represented climatically by Denver, and a "South" region represented by 

Phoenix, with the relative weightings determined by population data and geographical proximity. 

For example, EUIs in the West North Central census division is determined by a weighted 

aggregation of the results fiom the Detroit and Minneapolis climate locations: 

The weighting matrix shown in Table 4 would be used to weight the simulation runs by climate to 

develop EUIs for each sub census division for otherwise identical buildings. 

Building Level Aggregation of EUI Data 

The EUIs established for the 6 building core, perimeter, and floor-type areas in the model are 

weighted by their corresponding areas in the general population of buildings of each particular 

building type and in each census division. This weighting results in average whole-building EUI 

values for each sub census division and building type and for each combination of wall-type and 

heating fuel type. 

Characterization of CBECs building floor space data for weighting 

Each of the zone level EUIs from the BLAST simulations would be scaled to a representative 

floor space fi-action in the national mix of new construction. To do that, we would first 

characterize the population of buildings of each representative building type in each census division 

using the 1995 CBECS dataset. The population of buildings belonging to each representative 

building type would be examined at the census region level in order for the sample size to be 

considered significant for each building category. The building floor area and number of floors for 

buildings would be extracted for that population of buildings in each category, and a characteristic 

aspect ratio for each of the buildings in the population would be used to characterize the relative 

floor area for each of 6 possible combinations corresponding the zones in the simulated model: 

Upper floor, perimeter zone Upper floor core zone 

Midlevel floor, perimeter zone Midlevel floor, core zone 

Bottom floor, perimeter zone Bottom floor, core zone 

As aspect ratio is not captured in the 1995 CBECS data, the average aspect ratio would be 

developed for each building type based on the 1992 CBECS data set. As with the simulated 

model, the perimeter depth would be assumed to be 15 feet in the characterization of core and 

perimeter area. 

 6450-01-P COMMENT 02FF
Aggregation of Building-Level EUls across wall-type and heating fuel 

The building level EUI data from the previous step would be characterized by wall-type and 

heating fuel type weighting fraction established for each of the 7 building types using CBECS data. 

Development of wall-type and heating type weighting fractions 

Wall-type (mass or lightweight walls) and heating-type (electric resistance or fossil fuelheat pump) 

weighting fractions for each representative building category are developed based on CBECS data 

for each building type. The weighting fractions are believed to be statistically significant for all 

building types at the census region level. Because heating fuel in particular may vary significantly 

across census region, we propose to explore weighting heating fuel type for specific, common, 

building categories at the census division level. 

Aggregation across representative building type 

After accounting for wall type and heating type usage, the whole building EUIs for each 

representative building type in each sub-census division are weighted by the relative floor area for 

each building type. The relative floor area for all building types would be established based on 

CBECS data at the census division level. The subsequent weighting would result in typical whole 

building EUIs estimates for all buildings in each sub census division. 

At this point in the analysis, the whole-building EUIs data would also be converted to energy cost 

intensity (ECI) to correctly account for the variation in energy cost over the sub census region. 

Estimates of fuel costs would be based on data tabulated by the Energy Information Agency (EIA) 

for commercial building energy costs. 

Aggregation of average building EUlS and ECI data across sub census 

division by construction volume weighting 

The whole building EUIs estimates at the sub census division level would be weighted by estimated 

commercial building construction volume. EIA publishes estimates of total commercial building 

construction valuation for each census division as well as provides estimates of construction 

valuation at the state level that account for approximately 80% of the national total. This data 

would be used to weight the average whole building EUIs and ECI data from the sub census 

division level to national totals representative of new building EUIs (by fuel type) and ECI for 

buildings built to either ASHRAE Standard 90.1-1 989 or Standard 90.1-1 999. In addition, the 

national EUIs by fuel type would be converted to source EUIs using EIA national conversion 

statistics. 

 6450-01-P COMMENT 02FF
REFERENCES 

American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (ASHRAE). 1995. 1995 

ASHRAE Applications Handbook. Atlanta, Georgia. 

Barwig, F.E., et al. 1996. Preliminary Findings; Analysis of Commercial Space-Conditioning and 

Storage Water-Heating Equipment Efficiencies. PNNL- 1 1 1 9 1, Pacific Northwest National Laboratory, 

Richland, Washington. 

BLAST Support Office (BLAST). 1991. Volume I: BLAST User Manual. University of Illinois at 

Urbana-Champaign, Illiois. 

Energy Conservation and Production Act (EPCA). 42 USC 6834 et seq., as amended. 

Energy Information Adminiitration (EIA). 1994. 1992 Commercial Buildings Energy Consumption 

Survey. U.S. Government Printing Office, Washington, D.C. 

Energy Information Administration (EIA). 1998. 1995 Commercial Buildings Energy Consumption 

Survey Dataset. U.S. Government Printing Office, Washington, D.C. 

Energy Policy Act (EPAct) of 1997. Public Law 102-486, 106 Stat 2776. 

Friedrich and Messinger, 1995 "Method to assess the gross annual energy savings potential of 

energy conservation technologies used in commercial buildings", ASHRAE Transactions 

101(1):444-453. 

R.S. Means Company, Inc. (Means). 1997. MEANS Construction Cost Data. 20th edition. Kingston, 

Massachusetts. 

National Appliance Energy Conservation Act (NAECA) of 1987. Public Law 100- 12. 

 6450-01-P COMMENT 02FF
Appendix A 

Selection of Representative Climates 

 6450-01-P COMMENT 02FF
SELECTING REPRESENTATIVE CLIMATE STATIONS 

FOR USE IN A BUILDING ENERGY MODEL 

D. L. Hadley 

R. E. Jarnagin 

May 1993 

Prepared for 

the U.S. Department of Energy 

under Contract DE-AC06-76RLO 1830 

Pacific Northwest Laboratory 

Ri chl and, Washington 99352 

 6450-01-P COMMENT 02FF
SELECTING REPRESENTA'ITVE CLIMATE STATIONS 

FOR USE IN A BUILDING ENERGY MODU 

Donald L. Hadley 

Senior Research Scientist 

Ronald E. Jarnagin 

Senior Research Engineer 

Pacific Northwest Laboratory 

Richland, Washington 99352 U. S .A. 

The U.S. Department of Energy @0E) and the American Society of Heating, Refrigerating 

and Air-conditioning Engineers, Inc. (ASHRAE) are in a continual cycle of revising and 

updating their respective commercial building energy standards. These standards [1,2] 

specify energy-efficiency measures (EEMs) that are technologically feasible and economically 

justifiable (i.e., that are cost-effective over a building's life cycle). To help DOE and 

ASHRAE assess the impacts on energy consumption and energy costs, the Pacific Northwest 

Laboratory is refining a model that estimates changes in nationally aggregated building 

energy-use intensities (EUIs) resulting from proposed changes to the energy standards. One 

prerequisite for this model is the identification of a set of climate zones and representative 

climate stations covering the full range of unique climatic conditions occuning over the 

contiguous United States. 

The impact model is based on parametric computer analyses of the hourly simulation of 

prototypical building performance for one or more climates. Regional EUIs generated from 

the model are then aggregated, with appropriate weighting for population, building growth, 

and other factors, to a single national Em. For the results of the model to be applicable at 

the national level, however, the initial choice of climate characteristics for each region must 

cover all of the representative climates. 

Unfortunately, there is no consensus as to the correct number of climates or the delineation 

of climate zones. The identification of 'the specific climate zones andlor the selection of 

representative climate stations are frequently without solid technical justification and 

arbitrary, based on the researcher's previous experience, and, all too often, limited by 

availability of data. For example, a recent evaluation [3] of the effectivhess of the DOE 

commercial building energy standard was based on 10 commercial building types in six U.S. 

locations (Seattle, Washington; Los Angeles, California; El Paso, Texas; Lake Charles, 

Louisiana; Washington, D.C.; and Madison, Wisconsin). These six were a subset of the 

eight stations originally identified by Bowen et al. [4] as encompassing the full range of 

 6450-01-P COMMENT 02FF
climate variations found in the contiguous United States. However, other researchers have 

used different stations as being "representative. " 

Andersson et al. [5] described a methodology by which relatively homogeneous climate 

regions appropriate to a specific application -- in this case, building energy -- could be 

generated. The basic premise of the work is that it is possible to objectively aggregate 

specific U.S. population centers into common groups based on climate elements that most 

directly influence building energy consumption. The three elements selected were 

temperature, humidity, and solar radiation, represented as four variables - heating degreedays, cooling degree-days, latent enthalpy hours, and an annual average clearness index. 

The authors presented the results of a number of aggregations ranging from 5 to 24 regions. 

Clearly, 5 regions were too few, as many dissimilar sites were forced into the same climate 

region designation. A more reasonable number of climate regions appeared to be in the 

range of 11 to 15. Twenty-four climate centers produced a number of single-station climate 

regions. A limitation of this methodology is that the user selects an initial set of climate 

centers around which all other stations are aggregated. The specific sites and the number of 

climate centers are arbitrary and user-specified. Effective user specification requires a prion 

knowledge of relevant climate regions. 

'4. 

This paper presents a procedure referred to as synoptic climatology indexing for 1) objectively defining a unique set of climate zones for a specified geographical area, and 2) 

identifying the single most representative climate station for each zone, with the underlying 

climate database defined by the user. The procedure provides a sound technical basis for the 

selection process and does not require previous knowledge of the climate classification. 

2.0 METHODOLOGY 

The synoptic climatology indexing procedure uses a hierarchical clustering technique to 

group climate stations into climatologically homogeneous groups. A group, or cluster, of 

stations is considered homogeneous if its members are more similar, on average, to others in 

the cluster than to those in other clusters. This methodology is similar to that used by 

Andersson et al. [5] to aggregate U.S. population centers, except that it is not necessary to 

initially specify a climate center around which other sites are aggregated. 

This methodology is based on the objective synoptic climatology classification methodology 

first introduced by Kalkstein and Comg&.^[q and since used widely in a number of diverse 

applications. Hadley [q applied the procedure to identify homogeneous weather day-types in 

an analysis of daily variation in heating and air-conditioning energy use. Kallcstein et al. [8] 

used the approach to show signs of climatic change in the western ~orth%merican Arctic. 

'. A detailed technical report describing the development of the methodology and results will be 

available in late 1993. 

 6450-01-P COMMENT 02FF
tic cThis methodology uses a hierarchical clustering technique with a distance metric computed 

using an "average linkage." The average linkage computation is designed to minimize 

within-cluster variance and maximize between-cluster variance. This option in the 

computation produces the most realistic climatological groupings. It compares the average 

squared Euclidean distance between all possible pairs of observations between all 

combinations of stations/clusters. The clustering process is iterative; two stations/clusters 

with the minimum average distance between them are merged to form a single new cluster at 

each iteration. The merging continues until the final two are combined in the final iteration. 

A detailed description of the clustering procedure is presented in IUkstein et al. 193. Cluster 

analysis routines are readily available in many statistical analysis packages for personal 

computers. 

The clusters contain stations that are climatologically similar and that constitute a unique 

climate zone. A number of techniques are available to rigorously determine the number of 

clusters naturally occurring in the data set [6,9]. In theory, there is an identifiable 

breakpoint at which dissimilar (unlike) clusters begin to be merged and the within-cluster 

variance increases sharply. The "cqmct" number of natural clusters occurs just prior to this 

point. If more than the "correct" number of clusters are generated, the natural clusters will 

be artiiicially split into two or more similar clusters. If the number of clusters decreases 

below the "correct" number, two or more dissimilar clusters are forced together. In 

practice, this point is not easily identified. Other factors may also constrain the final number 

of climate zones selected. 

The final step in the synoptic climatology indexing is a ranking of all stations in a climate 

zone to determine the single station that best represents the zone's mean climatic 

characteristics. The metric used for this ranking is a composite 2-score statistic. A Z-score 

of 0.0 means that the variable for that station is equal to the average of that variable; a Zscore of 1.0 means that it is one standard deviation from the mean. 

The composite 2-score (23 is the average of the absolute value of the individual 2-scores 

(Zi) for that station. It is computed as follows: 

where Zi = ( X, - u ) / a 

X, = individual climate variable 

u = mean 

a = standard deviation 

n = number of variables. 

The station with the lowest composite Z-score is the station most representative of the 

climate for that region. 

 6450-01-P COMMENT 02FF
Climate Charactenzahon . . 

Typical Meteorological Year (TMY) data for 209 continental U.S. stations were used in this 

analysis. Because of the restriction in the impact model to develop climate zone designations 

consistent with the regional building characteristics data available only for the four major 

U.S. Census Bureau regions (West, North Central, Northeast, and South), each TMY station 

was first assigned to the appropriate region. 

The climate of each station was characterized by those variables that were used to develop 

the Alternative Component Package tables in the DOE and ASHRAE commercial building 

standards [1,2]. The 1 1 variables comprising the climate database were 

heating degreedays, base 18.3 " C (65 OF), (HDD 18) 

heating degree-days, base 10.O°C (50°F), (HDD10) 

cooling degreedays, base 10.O°C (5O0F), (CDD10) 

cooling degreedays, base 18.3"C (6S°F), (CDD18) 

cooling degree-hours, base 26.7"C (80°F), (CDH27) 

daily incident solar radiation on a north vertical face, w/m2, (VSN) 

daily incident solar radiation-on an east or west vertical face, w/m2, (VSEW) 

daily incident solar radiation on a south vertical face, w/m2, (VSS) 

diurnal range of temperature in the warmest month, OC, (DR) 

number of hours between 8:00 a.m. and 4:00 p.m. with temperature < 12.8 "C 

(5S°F), Or131 

number of hours between 8:00 a.m. and 4:00 p.m. with temperature between 12.8"C 

and 20.S°C (55°F and 6g°F), (120) 

In this analysis, all variables were equally weighted. 

3.0 RESULTS 

The synoptic climatology indexing technique was applied to the selected climate database. 

This analysis was performed initially for the continental U.S. stations only, so stations in 

Alaska, Hawaii, and the islands of the Caribbean and South Pacific were not included? In 

our situation, final selection of the number of climate zones used subjective judgment, based 

on an analysis of different levels of clustering (ranging from 5 to 30 zones) and an 

examination of the average distance be&& clusters merged at each successive iteration. 

We were also constrained by the need to limit the climate zones to a manageable number. 

Subsequent work in finalizing the national energy model would require $&ainetric 

simulations of hourly building energy use for up to 10 different building types for each 

Addition of these stations outside the continental U.S. to the cluster analysis is not expected to 

significantly affect the resulting clusters. 

 6450-01-P COMMENT 02FF
climate zone identified, and for a variety of building parameters. The indexing methodology 

is a simplified process that yields good representative results. It is well suited to situations 

involving large and diverse building stock. If all 209 stations were used without being 

filtered by some measure of significance or relevance, outlier stations could skew the results 

of the national energy model. 

'Pesultant Climate Zones 

The synoptic climatology indexing resulted in the identification of a unique set of climate 

zones for each of the four census regions. The final number of different climate zones 

selected ranged from two.in the Northeast region to six in the West region. The number of 

climate zones is dependent on the climatic diversity of the region. The extent of each zone is 

shown in Figure 1. To delineate the approximate geographic extent of each of the climate 

zones, a line has been loosely drawn between adjacent stations in different climate zones. 

However, extreme care must be taken in the extrapolation of climate zones in mountainous 

terrain. 

tion Rankin? and Selection 4 

A climatologically representative station for each of the climate zones was determined using 

a composite 2-score, identifying the one station whose climate characteristics most closely fit 

the average of all stations in that climate zone. To illustrate the process, climate parameters 

and individual 2-Scores are listed in Tables 1 and 2 for the six cities in the West region, 

Climate Zone 13. These stations have been ranked in ascending order of composite 2-score. 

In this climate zone, Phoenix was identified as the most representative station, with a 

composite 2-score of 0.596. 

Because the intent of the energy model is to estimate national impacts of proposed changes to 

EEMs in commercial buildings, the final climate station selection also included a 

consideration of population and new building construction. Stations in remote, lowpopulation areas with little new construction activity were not included in the frnal station 

selection. For example, in the West census region, Denver was selected over Cedar City 

(Climate Zone 1 1), and Los Angeles was selected over both Sunnyvale and Santa Maria 

(Climate Zone 15). 

.: s%, .r. ' 4.0 CONCLUSION 

The synoptic climatology indexing procedure described in this paper is successful in 

objectively identifying unique climate zones. The procedure eliminates %by of the 

limitations and personal biases introduced by the more common subjective approaches to 

identifying climate zones and selecting representative climate stations. Although developed 

specifically for application to data for the continental United States, the methodology has 

global applicability whenever it is necessary to define a set of climate zones. The technique 

is "data-neutral"; that is, it is viable regardless of the underlying climate variables selected or 

 6450-01-P COMMENT 02FF
whether the database is regional, national, or global. The user is free to select climate 

variables appropriate for the intended application, and can apply weights to the individual 

variables to emphasize a specific climate element or elements, depending on the goals of the 

application. 

 6450-01-P COMMENT 02FF
Table 1. Climate variables for cities in Climite Zone 13, West Region. 

:t 

P * Table 2. lndividuitl and composite Zscorq for cities in Climate Zone 13, West Region. 

* Indieales a major metropolitan.$rea with total popubttion exceeding 2SO,lt(H1. 

 6450-01-P COMMENT 02FF
Representative CI iraate Cities 

Figure 1. lacation of climate zones and representltive stations for each zone. 

 6450-01-P COMMENT 02FF
REFERENCES 

[I] U.S. Department of Energy. 1989, 10 CfR 435 Subpart A, Energy Conservation 

Voluntary Pe@onnance Standards for Commercial and Multi-Family High Rise 

Residential Buildings; Mandotory for New Federal Buildings; Interim Rule, 54 FR 

4554, January 30, 1989. Superintendent of Documents, U.S . Government Printing 

Office, Washington, D.C. 

[2] American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. 

1989. ASHRAE/IES Standard 90.1-1989, "Energy Efficient Design of New Buildings 

Except Low-Rise Residential Buildings. " ASHRAE, Atlanta, Georgia. 

[3] Hadley, D.L., and M.A. Halverson. 1992. Energy Conservation Potenrial of the 

U.S. Depamnent of Energy lnierim Commercial Building Standards. PNL-7967, 

Pacific Northwest Laboratory, Richland, Washington. 

[4] Bowen, W.M., S. Moreno, and A.R. Olsen. 1982. Climate Selection and 

Development of Climate Indicators. PNL-4435, Pacific Northwest Laboratory, 

Richland, Washington. 4. 

[5] Andersson,B.,W.L.CarroU,andM.R.Martin. 1986. "AggregationofU.S. 

Population Centers Using Climate Parameters Related to Building Energy Use." J. 

Climare Appl. Meteor. 25596-614. 

161 Kalkstein, L.S., and P. Comgan. 1986. "A Synoptic Climatological Approach for 

Geographical Analysis: Assessment of Sulfur Dioxide Concentrations. " Ann. Assoc. 

Amer, Geogr. 76:381-395. 

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