## Proposed Methodology for a ComparativeAnalysis of ASHRAEIIESNA Standard 90.1-1989 and Standard 90.1-1 999

Introduction

This document details a proposed method ofanalysis for the U.S. Department ofEnergy's (DOE) determinationof energy savings regarding BSR/ASHRAE/IESNA Standard 90.1-1999. The contentsofthis document include a description ofthe proposed methodology to be used for the determination, details assumptionsor proposed data sources, and elaborates on particular issues in doingthe analysis.



## Background

The Energy Policy and Conservation Act (EPCA) as amended by the EnergyPolicy Act of 1992, requires individual statesto adopt commercialbuilding energy standards that meet or exceed the energyperformance ofANSI/ASHRAE/IESNA Standard 90.1-1 989. When this standard or any later version ofASHRAE Standard 90.1 is amended, the U.S. Department ofEnergy (DOE) is required to do a determination ofenergy savings comparingthe amended version and previous version ofthe standardto determine ifthe amended version provides greater energy savingsthan the previous version. Ifa determination ofenergy savings is positive, individual stateshave 2 years after publication of such a determination to certifyto DOE that they have updated their state commercialbuilding energy codes to meet or exceed the efficiency ofthe modified ASHRAE Standard 90.1. Section 3049b)(2)(A)ofEPCA specifiesthat DOE has 12 months to make this determinationfkom the time the ASHRAE Standard 90.1 is amended.

In June 1999,ASHRAE approvedBSWASHRAMESNA90.1-1999, a completelyrevised version of the ASHRAE Standard 90.1. Accordingly,DOE conducted a preliminary analysis of energy savings in office and retail buildings. Thesebuilding types together represent 37.4% of building energy use in the U.S. That analysis compared buildings ofa single defined size and shape, but explored variation of savingswith climate, wall construction, and heating keltype. The analysisproposed here would provide a more detailedto look at the energy efficiency ofthe two standards across a broader range ofbuilding types and sizes characteristicofeach building type. The analysiswould provide a better representation ofthe improvement in commercial building energy efficiency between the two standards.


## Methodology Overview

The methodology used to assess the relative energy efficiency ofASHRAE Standard 90.1

## - 1989 and ASHRAE Standard 90.1

## - 1999would be based on both a qualitative comparison of thecriteria in the two standards and a quantitative estimate ofenergy savingsbased on those criteria.



### Qualitative 

### Comparison

A qualitativecomparison ofthe requirementsin each version ofASHRAE Standard 90.1 allows for a comparison ofthe relative energy efficiencyofthe standard in key areas and for key requirements with significant impact on building energy use. The qualitative comparison allows the relative stringency ofmany sections ofthe standardsto be quickly compared. In additionto the comparison of stringency, we propose to discuss some ofthe background behind the development of Standard 90.1- 1999and how the goals set early in the development process influencethe scope and stringency ofindividual requirements in Standard 90.1

### - 1999.

The proposed analysisplan provides for a qualitativecomparison ofthe stringencyofthe two versions ofASHRAE Standard 90.1 in each ofthe following areas:
Scope ofthe 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 ofthe envelope, lighting, and mechanical sections, the emphasis ofthe qualitative comparisonwould shift to focus on the particular requirements ofeach section, the difference in coverage ofbuilding components in each section, and the relative stringencyof comparablerequirements.

In the building envelope section,the comparison would focus on the impact ofthe different building enveloperequirements on the building heating and cooling loads for different building types and climates. The envelopecomparison would examine requirements for all envelope components, including roofs, walls, floors, and fenestration as well as explore variations in requirementsby 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 separatelyat the whole building and space-by-space lighting requirements in both standards in a variety ofcommercial building types, as well as examine the effect ofany additionallighting power allowances in the two Standards.

The building mechanical section comparison would be broken down into comparisonsof equipment efficiency requirements and system design requirements. Comparison ofthe equipment efficiency requirementswould be relatively straightforwardbetween both standards, although there would be changes in equipment covered. The system design requirements affect both the system efficiencyas well asthermal loads, and in addition may have direct energy impacts due. forinstance, to fan system requirements. Relative stringency and estimated impact on building efficiency would be prepared based on practical application ofthe system design requirementsin each standard. Variations in coverage ofequipment 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 stringencyof alternate paths internalto a single code or between codes. Rather we propose to focus on what we believe is the most common approachto using the standards or codes in question.



### Quantitative 

### comparison
The purpose ofthe quantitativeanalysis is to provide DOE an evaluation ofthe relative energy efficiencyofthe two versions ofASHRAE Standard 90.1 when taken as a whole, as well as an estimate ofthe energy impact ofthe change in standards on commercialbuilding energy efficiency.The quantitative comparison ofenergy codeswould be based on whole building energy simulation ofbuildings built to either the ASHRAE: Standard 90.1-1989 or 90.1-1999. It is not feasibleto simulate all possible permutations ofbuilding design, nor is the data availableto correctly weight each possible permutation in each possible U.S. climate as a &actionofthe national building construction mix. Hence, the quantitativeanalysis focuses on development ofa set of assumptions that characterize a particular subset ofthe building population, simulationof buildings that represent that subset, and provision for aggregation ofthe energy savingson a national level. Where there is concern about aggregationweighting, limited sensitivity analyses would be performed to explorethe variation ofenergy performance within a population subset.

It is recognized that there would be specific requirements ofthe standard that would not be amenable to simulationwithin the scope ofthis analysis. This is particularly true of system design issues in the mechanical sectionsofthe standards. For most ofthese 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 15zone building prototype developed for the Department by Pacific Northwest National Laboratory (PNNL). The energy use &omthe simulations would be scaled to correctly reflect energy use in the mix ofcharacteristicbuilding sizes and shapes for each representativebuilding type. Data on building size and shape would come &omthe Commercial Building Energy Consumption Survey (CBECS) Database (EIA 1998,EIA 1994). The simulations would be performed on seven representative building types in 11representative U.S. climates.
Energy Use Intensities (EUIs) developed for each zone in each representativebuilding would be weighted by total national square footage in each representative building category to provide anestimate ofthe national energy savings (Friedrich 1995). Note that only changesto new buildings would be considered in the quantitative analysis;the scope ofASHRAE Standard 90.1-1999 also addresses additions and renovationsto existing buildings. While there may be significant energy savings fiom applying an energy standard to building renovations, it would be extremelydifficult to quantifLthis impact. This proposal would only examine the impact ofASHRAE Standard 90.1-1999 onbuilding renovations in the qualitativecomparison ofthe two standards.

The quantitative analysis is broken into five steps:
1)Describebuildings and climates for analysis 2) Developbuilding models with characteristics for each standard level 3) Simulatebuilding prototypes in each climate 4) Scale energy use fiom simulated buildings to all buildings ofrepresentative type 5) Aggregate energy use across building categoriesto national energy use estimate

In step (I), the buildings types and climate zones for analysis would be developed. We propose to
use seven representativebuilding types which together represent approximately 80% of commercialbuilding energy use in the U.S. (EIA 1998) A description ofcommercialbuilding energy use and the choice behind these seven representativebuildings is discussed in a later section ofthis plan. The buildings would be simulated in 11U.S. climates. These 1 1 climates were chosen based on a statisticalclustering analysisof 11 distinct climate parameters important to building energyuse. The climate data set was the 232 TypicalMeteorological Year (TMY) hourly weather files compiled by the National Oceanographic and Atmospheric Association (NOAA). A briefdescriptionofthe development ofthese 11representativeclimates is discussed in Appendix A ofthis 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 oftheprototype building models would be given values characteristicofeither ASHRAE Standard 90.1-1989 or Standard 90.1-1999.
In step 

### (3), each building prototype is simulatedusing 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 simulationofthe prototype buildings would be mapped to corresponding areas in the population ofU.S. buildings. To estimate the national impacts, we (a) map the climate locations onto regions, (b) estimate the weights each ofthe 15 prototype building

zones would have in a typical mix ofbuildings ofthat representative type in each geographic region, (c) estimatethe weight each building type has in each geographic region, and (d) estimate the fiaction ofeach year's new commercial constructionrepresented by the type and construction ofeach simulated building within 11 separate regions ofthe US.. The estimateofnew building growth in each region would come from EIA valuations ofcommercial construction in eachcensus division as modified by relative construction cost estimators for locationsin 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 equivalentto 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 actualbuilding populations.

In step (9,

### an estimate ofthe total national impact on energy use ofgoing fiom Standard 90.1- 1989to Standard 90.1

## - 1999would be made by weighting the zonal EUIs for each prototype simulated by the estimated square footage ofeach building zone in a national mix ofbuildings.



### Selection of the Representative Building Types and Climate Locations for the BLASTsimulation

Based on the review ofthe annual energy use ofprincipalbuilding types surveyedby the 1995 CBECS, a ranking ofthe building types by energy usage was developed and is shown in Table 1. Seven ofthe top eight building types (in terms of the magnitude ofannual energyuse) account for 78.4% ofthe cumulative total energy consumption ofall commercial buildings. Sincethey account for more than three-quartersofthetotal commercial building energy consumption, the seven building types listed in bold type in Table 1were selected as the representativebuilding 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

973 614 561 461

Other Percent ofTotal

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%CBECS characterizesHealth Care facilitiesas either inpatient or outpatient care. The former facilitiesare difficult to characterize in terms oftypical internal or ventilation loads, although a general trend is 24 hour operation in at least some portion ofthe building. The latter facilities are generally similarto office buildings in terms ofloads and hours ofoperation. It is proposed decided that the outpatient care/doctor's office segment ofthe Health Care building type be added to the mix ofbuildings using the "office" building model to represent space cooling, heating and water heating loads. This would bring the percentage ofthe energy consumption captured in this analysisto approximately 8 1%.

The national climate variations for the BLAST simulation would be represented by the same eleven climate locationsused 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 availablefor all ofthe original locations except Orlando, Florida. For this analysis, Tampa, Florida would be chosen asthe 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 ofthe country they represent are shown in Figure 1.

For this analysis, a single 15-zone prototype would be used asthe basis for all ofthe 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 actuallymodeled (e.g., office buildings), the internal loads, ventilation loads, and operation scheduleswould be modified to represent those characteristicof that particular representativebuilding type. The 15-zonebuilding model would be used to estimatethe 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 ofthe 15zones in the simulated model would then be scaled to representthe relative proportion ofeach ofthese zones in a national mix ofbuildings ofthat same building type, with the same representativeclimate, and with the particular characteristicsmodeled.



### Base Building Assumptions:

The preliminary office and retail building simulationswere based on a 3-story, 48,000 ft2building prototype, with each floor consisting of a 10,000

### ft2core zone and four 1,500 ft2perimeter 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 ofthe building. Each floor ofthe prototype building would be 13feet high. The top floor ofthe building would have a separatelyzoned 4-foot high plenum above a 9-foot high office space.

### Figure 1

### -US Map Showing Eleven 

### TMY Climate Locations (cities) and Corresponding Climate Regions (dark boundaries) and Nine Census Divisions (shaded areas) Spacecooling requirementsin the mode1would be met using 15 separate single-zone spaceconditioning systems. All building types with the exception ofwarehouse would be assumed to be heated to 70°F and cooled to 75OF during primary occupied periods. During setback periods, these buildings would be assumed to be heated to 55OF and cooled to 85OF. The present proposal is to simulatewarehouses 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 stakeholderinput on the fraction ofnew warehouse construction that would actually fall into this category. Ifit is deemed that a large fraction ofwarehouse construction would not fall into the semi-heated category, DOE may split this into two categories.

Equipment schedulesand 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 tradeoffmethod, metered commercialbuilding loads data, and CBECS reported building schedule data. Ventilation rates for the simulated buildings would be designed to meet ASHRAE Standard 62-1989requirements for both versions ofASHRAE Standard 90.1.

### Envelope Building Parameters



### Windows An average window-to-wall ratio 

### (WWR) for each building type would be extracted out ofthe CBECS 92 data set for each building type and census region. While not completed at this time, previous evaluationsofthe CBECS data set have suggested that for many building types, the window-to-wall ratio may vary substantially from the average value. Because ofthe significance ofthis 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 of40% in
two non-residential building type (office and education)to explorethe impact ofthis variation on the energy performance ofthe two standards for each ofthe 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 envelopetradeoff 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 ofthat standard.



### Opaque 

### WaIIsandRoofs It 

### is proposed to separatelymodel each building type using either a mass wall construction or lightweight wall construction. The ENVSTD 2.0 tradeoff softwarewould 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 roofrequirements would be taken from AppendixB ofthat standard. Metal-frame wall constructionwould be assumed asthe basis for lightweight walls for buildings built under ASHRAE Standard90.1

## - 1999. Metal-frame wall construction isbelieved to be the most commontype of lightweight wall construction currently in use for commercial buildings.

Table 2 showsthe 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°Fand 65OF base temperatures are also shown. For seven ofthe eleven climates, maximum U-value requirements for metal-fiamewalls 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 frequencyofthe lightweightwall types for construction in eachbuilding type and censusregion. 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 requirementsfor the "insulation entirely above deck" roof category. This categoryreflects the requirementsfor a typical built-up roof construction. CBECS construction data suggests that built up roofs are the most common construction type in commercialbuildings. Table 3 shows the maximum U-value

### allowed for each rooftype in 90.1-1999. For 10ofthe 11 locations, the "insulation above" deck U-value requirement is approximately 

### 2.5% less than that ofthe 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 ofheating is expectedto 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 rooftype. It is recognized that not modeling roofs with attics would introduce some bias

Table 2. Non-Residential Lightweight Wall U-values in 90.1-1999


# 1 I Metal ( Wood 

## I Metal 

## 1[

Table 3. 

### Non-Residential Roof U-value Requirementsin 

### 90.1

## -1999

Bintoward underestimatingthe energy savings with the 90.1-1999 standard for buildings which do

use this rooftype.

We are seeking fkrther input about the relative kequency ofthe roof construction types for each building design. There would be no attempt to explore the solar absorptivity/U-valuetradeoff in ASHRAE Standard 90.1-1999 in the quantitativeanalysis, primarily due to the difficulty of determining the 

### frequency ofuse ofthis tradeoff in fkture construction as well asthe technical difficultyofcorrectly evaluating the impact ofthe tradeoffon different roof constructions.

Floors Floors would be modeled as slab on grade construction for all buildings, and differencesin slab insulation requirementsbetween the standards would not be modeled. None ofthe whole building simulation software availableare believed to adequatelymodel ground-heat transfer effects, and no attempt would be made to model them for this exercise. It is proposed that the relative stringencyof slablfloorinsulation requirements be examined only in the qualitativeanalysis.



### Lighting Loads

We propose to use whole building lighting power density (LPD) values for the quantitative analysisportion ofthe Determination. A quantitativeanalysis 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 ofthe complexitiesofthe space-by-space method and the fact that it relies on specificbuilding characteristics that are unknown in both standards, the whole building method is proposed here asthe only practicalmethod for a comparison across a wide variety ofnew building construction. In addition, it is believed that the whole building approach is likelythe most common method used for establishing compliance with either standard, as it essentiallyentailsonly a count offixtures 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 correspondingUPD values in ASHRAE Standard 90.1989 are also meant to include all building lighting but are presented in a set ofbuilding size ranges. With the exception ofchoosing an appropriatebuilding size range for the Standard 90.1 1989whole building requirement based on average or typicalbuilding 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 generallyequitable in terms oftheir inclusion ofall space lighting. However, there are two major differences between the two Standards:

1. The ASHRAE Standard 90.1-1989 retail spaceLPDs include potential merchandise display lighting. The ASHRAE Standard 90.1-1999 values do not - in Standard 90.1

### - 1999, finemerchandise 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 aretreated 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 ofthe Standard 90.1

## - 1999additional lighting allowance for these space functions would exist in the marketplace is not possible with the available data.



### 2. The Standard 90.1999 LPDs incorporatethe effects of expectedtypical room geometry on the effectiveness oflighting fixtures which effectsthe required LPD value. This is done by incorporating into the calculationprocess a lighting Room Cavity Ratio (RCR) determined to be appropriatefor each space by professional consensus. Standard 90.1

## - 1989accountsfor room geometry effectsby applying an Area Factor (AF) to each space's LPD value. The AF is

a fknction ofthe room geometry. Because the actual room geometry and therefore the exact AFs are not 

### known for the set ofreal buildings in the marketplace, it is not possible to make

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

Theseprimary differences in the application of space-by-space values in standards compliance makes any comparisonsusing them difficult and open to much interpretation. Using the whole building allowance avoidsthe 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 oflighting requirements for specific space types as shown in the two standardsby convertingthe Uniform Power Densities (UPDs) in ASHM Standard 90.1

## - 1989to directly comparable Lighting Power Densities (LPDs) using room geometry factorsthat are effectivelythose used for ASHRAE Standard 90.1

## - 1999 development. This would allow comparisonofthe lighting power densities for the same set of spacesthe 90.1 lighting subcommitteeused in the development ofASHRAE Standard 90.1-1999. The qualitative analysis would also discussthe relative impact ofthe "additional lighting power allowances" for specific applications. It is proposed to examinethis by calculatingthe 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

## - 1989for the 90.1

### - 1989retail space categoriesassumed in the lighting section ofthat standard. The fiaction ofareas for the Standard 90.1-1999 allowanceswould be would be compared to estimates by lighting designers and others ofwhat would be characteristicusage areas for these retail space types.

### Mechanical Equipment

As discussed,the heating and cooling loads in the simulationmodels would be assumed met using single zone cooling and heating equipment for each building type (packaged DX systemsand either gas or electric furnaces). The performanceofeach system would be established as follows:



### Cooling System Efficiency: The improvementsin 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 ofchanges in equipment efficiencyacrossthe many categories ofpackaged cooling equipment,the equipment efficiency modeled for each standard would be representedby an estimated load-weighted average equipment efficiency based on a combination of shipmentvolume weighting and anticipated load for each piece ofequipment. The product ofthe shipment volume, a characteristic equipment capacity, and the characteristicfull 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 ofthe

seven building types through recent work in DOE'SEPACT 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 lessthan 65,000 Btulh cooling capacity in the estimate ofaverage 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 energyusage by supplying the hnlmotor efficiency and static pressure. We would then have BLAST calculatefan power using the airflow rate fiom the system design calculations. The compressor energyusage at design conditionswould be characterized by the "design full load power ratio" (DFPLR) which we would enter as a user input. The DFPLR is essentiallythe same

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

DFPLR = (1-R) / (EERf3.413

## +R)

where R is the ratio of supply fan power to full load power at nominal design test conditions. Based on a review ofexisting products, R has been assumed equal to 0.10 for equipment meeting the nominal efficiency requirements specified in the standard.GasFurnaces: Spaceheating equipment efficiency would be modeled using an estimated thermal efficiencyfor the furnace. Standard 90.1 specifies minimum combustionrather 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 90.1-1989. This value represents the estimated shell loss rate for commercial furnaces assumed in the Standard 90.1

### - 1999 equipment efficiency analysisas 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 ofASHRAE: Standard 90.1-1999. Note that these loss rates are measured under laboratoryconditions. 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 (seeU.S. Code ofFederal Regulations 10CFR430Subpart B Appendix N). The thermal efficiency for furnaces would thus be calculated as:

ThermalEfficiency =CombustionEfficiency 

## - 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 ofthe 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 representativeofenergy use with arbitrary sizes ofequipment,

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

### is a function ofthe number ofindividual furnaces used in the building and thus 

### is strongly dependent on how the building is zoned in real life. The simulator must also decide ifgas would be supplied to these furnaces after the heating season is over. Since ASHRAE Standard 90.1

## - 1989 allowsthe use ofa standing pilot and ASHRAE Standard 90.1

## - 1999 requirements do not allow the use ofa standing pilot light, not assuming a pilot for ASHRAE Standard 90.1-1989 standard would underestimate this increase in stringency ofthe standard, however it is believed that in the real world, the use ofpilot lights has become less common on this type ofheating equipment. It is noted that the relatively small increase in thermal efficiency for rooftop gas furnaces in ASHRAE Standard 90.1

## - 1999(-2%) is approximatelythe same asthat 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 ofthis 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 ofof$ce and retail buildings presented inJune 1999.
Water Heating Effiency Typical water heating efficiencieswould be based on a shipment weighted average ofefficiency, capacity, and full load equivalent operating hours as recently establishedby 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 characteristictank sizes for each equipment categoryas were used in the developmentofASHRAE Standard 90.1

### - 1999. The characteristic standby loss rate would then be normalized to the characteristic input rating.Relativepeak 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. Simplifiedprofiles 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 estimatethe average water heating energyuse 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 ofthe proposed ASHRAE standards for commercially sized service water heating equipment.

For the purpose ofthis 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 spaceheat would also use gas for heating servicehot water.

FanPower Both Standard 90.1-1989 and Standard 90.1-1999 regulate fan power through a fan system efficiency metric. Standard 90.1

## - 1989 specifies a maximum fan motor wattage (measured) per cfin ofdesigned air volume in the system. Standard 90.1-1999 by contrast, specifies a maximum motor nameplate horsepower per cfin ofdesign air volume. The purpose ofthe change to nameplate horsepower was to provide for easier inspection and compliance verification. The 90.1

### - 1999 requirements were developedto provide equivalent stringency with the 90.1-1989requirements, but include proper allowance for motor and drive eficiency, aswell asprovide a proper allowance in motor sizing for the discrete steps ofmotor horsepower available. For example, a designer may create a building fan system design that requires a calculated 2.8 brake horsepower/cfinto move a nominal 3000 cfin at the requisite static pressure. Sincethe 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 effectivelyno change in the actual fan energy use ofthe system.
Additionally, discussion with system designersduring the development of90.1-1999 indicated that the fan power limits in both versions of90.1 are higher than would be seen in many typical installations. Both the maximum wattage or nameplatehorsepower requirements are set conservativelyhigh 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 ofthis comparison, we would assume that there is no net change in base fan power requirementsbetween both versionsof Standard 90.1. However, there may be changes due to incorporationof system components. We proposeto 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 economizersis 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 systemswith a gas hrnace heating section (approximately0.2 in fan static), assuming a constant compressor performance

### (DFLPR) for systems with and without a gas heating section. Thus, for an officebuilding using gas hrnaces and economizers,the total fan static would be 2.50 in. H,O

Economizers We propose to model building systems incorporating economizersand not incorporating economizers in each climate. The economizerrequirements in both standards are functionsof system size and ofclimate. 

### In order to account for economizer usage, we propose to weight the EUIs for the economizer and no-economizer simulationsusing appropriate weights for each climate.

Table 

### 4. Fan Static Assumptions

Base SystemFan Static Building Type Adders SystemDesign 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 divisionsbuilding 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 representativebuilding 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 commercialbuilding construction established for each subcensus division

### 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 ofeach ofthe 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 he1type for each simulated building.



### Conversion of Zone EUI data to CorelPerimeterlFloor-level 

### EUI data

Each floor ofthe simulated building would have 

### 4 perimeter zones. The average perimeter zone EUIs would be calculated for each floor ofthe 

### 15 zone model, translatingthe zone-level EUI data for each ofthe 

### 15zones 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 ofhigh versus low fenestration areas, the underlying assumptionwe propose to use is that buildings in the general population are randomly oriented. This assumptionis also implicit in the description ofthe 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 estimateofthe fiaction ofcooling 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 ofpackaged equipment and would be based on data provided by ARI. For each ofthe 

### I I locations, we propose to estimate the total shipped capacity ofcooling equipment for which an economizer would likely be required and for which an economizerwould not be required under each standardusing the national mix of equipment shipments. For each climate, the fiaction of shipped cooling capacityrequiring economizerswould be used to weight the cooling energy EUIs fiom the economizerand no economizer simulations for otherwise identical buildings.

### 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 locationsto census data. This intermediate step would be necessaryto support aggregation across building types, since demographic information on existing building square footagewould be availableby 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 ofmultiple climate-location results within each census division For example,the Pacific census division is composed ofthree states: Washington, Oregon, and California. This censusdivision would contain all or part offive 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 determinethe relative influence ofthese five cities on the Pacific census division results. To do so,
we proposeto make use ofdata developed for the commercialequipment standards program which has defined a weighting matrix for eleven sub censusdivision as shown in Table 

### 5.

Table 

### 5 Aggregation Weights for Eleven SubCensus Divisions

This matrix was developed based on population data for metropolitan areas falling within geographicalregions correspondingto each climate location shown in Figure 1. The census definition ofthe Mountain and Pacific censusdivisionsare problematic in that each separately containtwo sub-regions that are have very wide disparities ofclimate and electricityprices. Generally,the warmer areas within these regions - California and the desert Southwest - have much higher electricityprices than the northern areas. Thus, the continued use ofthe conventional

census divisions would blur the relative economic impact that would result in these areas %omthe adoption ofnew 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 1000 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 1000 0 0 0

1000 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

1000 Pacific North 0 0 0 0 0 0 0117 0 0 884 0 0

1000level 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 geographicalproximity. For example, EUIs in the West North Central census division is determined by a weighted aggregation ofthe results fiom the Detroit and Minneapolis climate locations:

The weighting matrix shown in Table4 would be used to weight the simulationruns by climate to develop EUIs for each sub census division for otherwise identical buildings.



### Building LevelAggregation 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 generalpopulation ofbuildings ofeach particular building type and in each census division. This weighting results in averagewhole-building EUI values for each sub census division and building type and for each combination ofwall-type and heating fuel type.



### Characterization of CBECs building floor space data for weighting

Each ofthe zone level EUIs 

### from the BLAST simulations would be scaled to a representative floor space fi-actionin the national mix ofnew construction. To do that, we would first characterize the population ofbuildings ofeach representative building type in each census division using the 1995 CBECS dataset. The population ofbuildings belonging to each representative building type would be examined at the censusregion level in order for the sample size to be considered significant for each building category. The building floor area and number offloors for buildings would be extracted for that population ofbuildings in each category, and a characteristic aspect ratio for each ofthe buildings in the population would be used to characterize the relative floor area for each of6 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 characterizationofcore and perimeter area.

### Aggregation of Building-Level EUls across wall-type and heating fuel

The building level EUI data from the previous step would be characterizedby wall-type and heating fuel type weighting fraction established for each ofthe 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 representativebuilding 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 censusregion level. Because heating fuel in particularmay vary significantly

across censusregion, we propose to explore weighting heating fuel type for specific, common, building categories at the census division level. 

### Aggregation across representative buildingtype

After accounting for wall type and heating type usage, the whole building EUIs for each representativebuilding 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 censusdivision 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 censusregion. Estimates offuel costswould be based on datatabulated by the Energy Information Agency (EIA) for commercialbuilding energy costs.



### Aggregation of average building EUlS and ECI 

### data across sub census division by constructionvolume weighting

The whole building EUIs estimates at the sub census division level would be weighted by estimated commercialbuilding construction volume. EIA publishes estimates oftotal commercialbuilding construction valuation for each census division as well as provides estimatesofconstruction valuation at the state level that account for approximately 80% ofthe 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 ofnew building EUIs (by fuel type) and ECI for buildingsbuilt to either ASHRAE Standard 90.1-1989 or Standard 90.1-1999. In addition,the national EUIs by fuel type would be converted to source EUIs using EIA national conversion statistics.

### REFERENCES

American Society ofHeating, Refrigerating and Air-Conditioning Engineers, Inc. (ASHRAE). 1995. 1995 ASHRAE ApplicationsHandbook. Atlanta, Georgia.
Barwig, F.E., et al. 1996. Preliminary Findings; Analysis of Commercial Space-Conditioningand Storage Water-Heating Equipment Efficiencies. PNNL- 1119 1, Pacific Northwest National Laboratory, Richland, Washington.

BLAST Support Office (BLAST). 1991. VolumeI: BLAST UserManual. UniversityofIllinois at Urbana-Champaign, Illiois.
Energy Conservation and Production Act (EPCA). 42 USC 6834 et seq., as amended.
Energy InformationAdminiitration (EIA). 1994. 1992 CommercialBuildings Energy Consumption Survey. U.S. Government Printing Office, Washington, D.C.
Energy InformationAdministration (EIA). 1998. 1995 Commercial Buildings Energy Consumption SurveyDataset. 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 ConservationAct (NAECA) of 1987. Public Law 100-12.

## Appendix A

Selection of Representative Climates

### 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 

### chland, Washington 99352

### SELECTING REPRESENTA'ITVE 

### CLIMATE STATIONS FOR 

### USE 

### IN A 

### BUILDING ENERGY MODU

Donald 

### L. Hadley SeniorResearch 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-useintensities (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 technicaljustification 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 commercialbuilding 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

### 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 specificU.S. population centers into common groups based on climate elements that most directly influencebuilding 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 

### aprion 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 

### technicalbasis for the selectionprocess 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&.^[qand since used widely in 

### a number of diverse applications. Hadley 

## [qapplied 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%mericanArctic.



# '. A detailed 

### technical report describing the developmentof the methodology and results will be available in late 1993.

### 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 computationproduces the most realistic climatological groupings. It compares the average 

### squared Euclidean distancebetween all

### possiblepairs 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 

### (23is 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.

### 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 characteristicsdata 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 commercialbuilding standards [1,2]. The 11

### variables comprising the climate database were heating degreedays, base 18.3"C 

### (65OF), (HDD18) 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-onan 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 SouthPacific were not included? In 

### our situation, final selection of the number of climate zones used subjectivejudgment, 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.

### climate zone identified,and for a variety of building parameters. The indexing methodology is a simplifiedprocess 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 delineatethe 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, climateparameters and individual 2-Scores are listed 

### in Tables 

### 1and 

### 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 

### finalclimate 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 

### 11), 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 

### introducedby the more common subjective approaches to identifying climate zones and selecting representative climate stations. Although developed specifically for application to 

### data for the continentalUnited States, the methodology has 

### global applicabilitywhenever 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

### 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.Table 1. Climatevariables for cities in Climite Zone 13,West Region.

:t 

### P 

### * Table2. lndividuitl and composite Zscorqfor cities in Climate Zone 13,West Region.



### * Indieales a majormetropolitan.$reawith total popubttionexceeding 2SO,lt(H1.Representative 

### CI iraate Cities

Figure 1. 

### lacation 

### of climate

### zones and representltive 

### stations for 

### each zone.

## REFERENCES

[I] U.S. Department of Energy. 1989, 10 

### CfR435 

### SubpartA, Energy Conservation VoluntaryPe@onnance Standards for Commercialand 

### Multi-Family High Rise 

### Residential Buildings; Mandotory for 

### New Federal Buildings; Interim Rule, 54 

### FR 4554, January 30, 1989. Superintendentof 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-RiseResidential Buildings. 

### " ASHRAE, Atlanta, 

### Georgia.
[3] Hadley, D.L., and M.A. Halverson. 1992. 

### Energy Conservation Potenrial ofthe U.S.

### Depamnent ofEnergy 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. 

### ClimareAppl. Meteor. 25596-614.

161 Kalkstein, L.S., and P. Comgan. 1986. "A Synoptic ClimatologicalApproach for Geographical Analysis: Assessment of SulfurDioxide Concentrations." Ann. 

### Assoc. Amer, 

### Geogr. 76:381-395.
[7'J 

### Hadley, D.L. 1993. "Daily Variations 

### in WAC System 

### Electrical Energy Consumption 

### in Response to Different Weather Conditions.

### " Energy and 

### Buildings 19~235-247.
[8] Kallcstein, L.S., P.C. Dunne, and 

### R.S. Vose. 1990. "Detection of Climatic Change in the Western North American Arctic Using a Synoptic ClimatologicalApproach." 

### Journ. of Climate 3: 1 

### 153-1167. .: ,- 

### . [9] 

### ICalkstein,L.S.,G.Tan,andJ.A.Skindlov. 1987. "AnEvaluationofThree Clustering Procedures for Use in Synoptic Climatological Classification.

### " J. 

### Climate Appl. Meteor. 26:717-730.