Reference NBS

Publications A 111 DM 467^37

NBSIR 78-1525

Climate Data Abbreviation for

the Computerized Calculation

of Heating and Cooling

Requirements in Buildings

Edward A. Arens

Darnel H. Nall « tLC

Environmental Design Research Division

Center for Building Technology

National Engineering Laboratory

National Bureau of Standards

Washington, D C. 20234

December 1978

-QC

100

U56

78-1525

U S. DEPARTMENT OF COMMERCE

NATIONAL BUREAU OF STANDARDS

Msna! isrssi of SfantfarSI MAY 1 4 1979

NBSIR 78-1525

>i< y-- CLIMATE DATA ABBREVIATION FOR

THE COMPUTERIZED CALCULATION

OF HEATING AND COOLING

REQUIREMENTS IN BUILDINGS

Edward A. Arens

Daniel H Nall

Environmental Design Research Division

Center for Building Technology

National Engineering Laboratory

National Bureau of Standards

Washington, D C. 20234

December 1978

r

. CM AjMpifT k ) f) y5 / f?

U.S. DEPARTMENT OF COMMERCE, Juanita M. Kreps, Secretary

Dr. Sidney Harman, Under Secretary

Jordan J. Baruch, Assistant Secretary for Science and Technology

NATIONAL BUREAU OF STANDARDS. Ernest Ambler, Director
i' V?

w
FOREWORD

This is one of a series of reports documenting NBS research efforts in developing

energy and cost data and performance criteria for energy-efficient buildings

.

The work described in this report was supported by the Department of Energy

under the Building Energy Conservation Criteria Program, Task Order A0G8-BCS

to Interagency Agreement No. EA-77-A-01-6Q1 0.

c

iii
Abs tract

This paper documents the development of a climate data abbreviation technique

for building thermal analysis. The paper first discusses the characteristics

of computerized building thermal simulations and the requirements of abbreviated

data. The technique is then described together with the statistical analyses

used to develop it. A series of tests of the representativeness of the abbreviated climate data are documented. Finally, the limitations and potentials of

the abbreviation technique are discussed.

Key words: Climate data; computer; energy; load-calculation; residential;

weather

IV
ACKNOWLEDGEMENTS

We thank William Carroll and Charles Robinson, Thermal Engineering Program, NBS

James Filliben, Statistical Engineering Division, NBS, and Julie Pitts, Architectural Research Program, NBS, for their help in preparing and analysing the

data presented here.

c

v
CONTENTS

Page

1. Introduction 1

2. Computer Simulations 2

3. Abbreviated Climate Data 4

4. The Abbreviation Methodology 6

5. Implementation of the Algorithm 16

6. Analysis of Results 17

7. Conclusion 21

Appendix: Listing of Program to Select and Create

Abbreviated Weather Years . 51
1. INTRODUCTION

Building designers are now commonly analyzing building energy usage with computer

programs that calculate the building's thermal response for each hour of a complete

year. The results of these computations are used to evaluate alternative building

design and mechanical equipment schemes, to establish compliance with codes,

and to predict operating costs.

In addition to a complete description of the building, these programs require

a continuous year of hourly observations of weather conditions for the building's

location. Magnetic tapes with one year of recorded weather values for various

locations are made available by the National Climatic Center of the National

Oceanic and Atmospheric Administration, Data recorded on these tapes include

dry and wet-bulb temperatures, cloud cover and type, wind speed and direction,

barometric pressure and precipitation. Using each hourly set of these variables

in turn, the hour-by-hour program calculates the heat fluxes in the building.

Thus, in order to calculate the energy requirements for the annual cycle, the

program must perform 8760 separate sets of calculations. As a result, the program

often consumes large amounts of computer time and is relatively costly.

This paper describes a technique for reducing the time required for a yearly

analysis by selecting a smaller sample of hours to represent the entire year.

The technique is termed a climate data abbreviation and is incorporated into a

computer program which automatically reads the full year weather file and

creates a short verion. The characteristics of the hour-by-hour building thermal

simulation are presented first to demonstrate its value as an analysis tool

and its costs in computer time. Then the benefits and limitations of climate

c data abbreviation for reducing computer time requirements are discussed. The

1
paper describes the development of the technique through statistical analyses,

and gives the results of various tests of its performance. Finally, suggested

improvements and future work are discussed.

2. COMPUTER SIMULATIONS

The hour-by-hour computer simulation is currently acknowledged as the most accurate method of calculating heating and cooling requirements for buildings. [1]

A number of hourly simulation programs are available to researchers and practitioners either as software packages or through time-sharing organizations. Some

of the more widely used programs include:

*Calcon--DoE 1 University of California, Berkeley

TRACE The Trane Company

AXCESS Edison Electric Institute

E-Cube 75 American Gas Association

HCC III Automated Procedures for Engineering Consultants

NBSLD The National Bureau of Standards

NECAP National Aeronautics and Space Administration

BLAST U.S. Army Construction Engineering Research

Laboratory

These programs have been widely used in the recent past and users have made substantial investments for both acquisition costs and training costs. Any technique

for reducing the computational requirements in predicting the energy needs of

a building should recognize the widespread distribution of the currently available

programs

.

* was Cal-Erda before 1978

2
Most of these programs share algorithms that were developed under the guidance

of the ASHRAE Task Group on Energy Requirements and which have been published

by ASHRAE. [2] The,.algorithms permit the accurate modeling of the thermal

interactions among the various components of the building. The primary rationale

for the hour-by-hour simulation is that heat transfer phenomena in a building

are time dependent processes for which one hour is a reasonable computational

time step. Integration in steps smaller than the one hour period results

in little gain in accuracy except in some specific research applications. [3]

Lengthening the time step for the calculation would drastically affect the

ability of the program to quantify two of the most important energy conserving

phenomena, storage of heat over the diurnal period, and the correspondence

of the occupancy, lighting and temperature control schedules with the daylight

hours. The phenomena considered in most of these program includes not only

the conductive heat flux through the envelope elements, but also air leakage,

solar heat gain through translucent elements, solar gain on opaque surfaces,

heat storage effects, internal heat load and wind forced convection. Because

of the thermal interaction of the building elements »• a change in one element

for conservation purposes may alter the effects of these phenomena on the other

elements of the building. Reducing the complexity of the computer simulation

may result in a lack of sensitivity in the program for comparing similar

conservation strategies.

An unfortunate by-product of the complexity of the calculations in a building

thermal analysis program is the amount of computer time required and the resultant

cost of performing an analysis. In an ideal process of designing a building,

c analyses would be performed to evaluate the thermal consequences of each major

design decision. By knowing the thermal performance of each energy conserving

option on a specific building, the designer may select the most effective set

3
for that building. For example, during the predesign phase of the GSA Norris

Cotton Federal Office Building, in Manchester, N.H., National Bureau of Standards

engineers performed scores of simulation analyses to determine the ideal

aspect ratio, percentage of glass on each face, thermostat strategy, and

insulation placement for energy conservation. [4]

Unfortunately, currently available simulation programs are simply too expensive

to use routinely in a formal or even informal optimization process. For time

sharing users of these programs much of the high cost of their use is caused

by the large amounts of computer time needed to perform the analysis. Time

requirements for the analysis can be divided generally into initialization

time and calculation time. The calculation time is a linear function of the

number of hours to be simulated and accounts for between 90 and 95% of the

total time required. Thus a significant decrease in the computer time requirements can be achieved only by optimizing the algorithms within the computation

loop or by decreasing the number of iterations required to characterize an

entire year. Any method of achieving this decrease should be easily applicable

to most of the computer programs currently available.

3. ABBREVIATED CLIMATE DATA

A promising approach to the reduction of computer time and the associated costs

of hour-by-hour building thermal simulations is to abbreviate the climate data

tapes supplied by NOAA. In this approach, a sample of hourly sets of data

represents the 8760 hours of data on the NOAA tape. The integrated heating

and cooling requirements calculated by using this sample are scaled to yearly

requirements by the ratio of 8760 hours to the number of hours in the sample.

This approach fulfills the two requirements developed in the previous section:

4
(1) maintenance of the sensitivity of hourly analyses to time-dependent effects

of detailed architectural design options; (2) ability of the method to be used

with unmodified versions of the simulation programs currently available,, Reducing

the number of hourly repetitions required to characterize the year is not only simpler

and more universally applicable than optimizing the algorithms within the program

but is also a more effective, means of reducing the costs of computation. Reducing

the number of days used to characterize the year provides the additional benefit

of reducing the amount of output to be analyzed. Results of simulations using

a sufficiently abbreviated year may be presented in a simple graphic format,

with the characteristic daily profiles of loads and temperatures for each month

plotted on a single page.

The abbreviated weather data that represents the full year may either be synthetic

data generated from statistical summaries of the year, or may be a set of selected

segments from the hourly observations on. a weather tape. One advantage cited

for synthetic weather data in general has been that a weather tape for a new

location may be conveniently generated using data readily available in published

statistical summaries [12], However, certain subtleties of weather that may

be very important in determining the suitability of a building design to its

location may not be characterized in the available published summaries. The

analysis needed to incorporate such subtleties in synthetic data becomes quite

complex, and requires input that can only be gained by processing an actual

weather tape. Examples of daily subtleties important to building design are

consistent morning fogs, sea breezes, and afternoon cumulus cloud development.

On a seasonal basis, the correlation of high winds with low temperature during

cold front passages increases the effect of the low temperature on energy consumpQ

tion in buildings. In order to fully characterize a particular climate, the

synthesis must be able to capture the diurnal temperature range and distribution,

5
the distribution of cloud cover and wind over the day, and the correlations

of wind, humidity, cloud cover, and temperature which are distinctive to the

location.

Actual data on the other hand can be assumed to demonstrate, over the course

of an appropriately sized sample, some of the subtle characteristics of the

local climate. This is particularly true for diurnal distributions of climate

variables. A NOAA weather tape in either TD 1440, TRY, or SOLMET format

may be used to provide the base data from which the sample is selected. The

means for selecting the sample should be designed to insure that the calculated

heating and cooling requirements of varying building configurations using the

sampled data are as close as possible to those of the full year. In addition,

the technique should assure that the sample contain both the peculiarities and

the extremes of the local climate, because these may have important ramifications

on building design in that area.

4. THE ABBREVIATION METHODOLOGY

The abbreviation technique developed here uses segments of actual weather data

to make up the short year. The time units in which both climate reports and

load calculation programs summarize data determine the general format of the

abbreviated short year tape. For example, the NBSLD program outputs both a

daily and monthly summary in addition to its hourly output file. Long-term

climate summaries published by NOAA report monthly averages. Therefore, it

is convenient for the short weather tape to represent each month, using a

shorter interval of days. The simulation using such a short year provides

not only total yearly results but also an indication of monthly results.

Averages for weather variables within the sample may be compared with averages

for the month from the full year tape and also with long term averages from

6
published sources. If the sample selection methodology uses monthly averages

in selecting the sample its user has the opportunity to select a sample representative of long-term average climate from a single or limited number of yearly

weather tapes. The input data for the selection process in this case would be

taken from the long-term monthly averages published by NOAA. Representation

on a monthly basis also presents other advantages. The scale of weather variation

within a month is small compared with that of a full year, so that the varying

solar geometry throughout the year is well represented by including samples

from each month, and the general boundaries of the heating and cooling seasons

may be determined.

In this abbreviation technique, the days selected to represent each month are

contiguous in order to minimize the discontinuities created when disjunct hours

of climatic data are juxtaposed. The only discontinuities created by this

abbreviation process are then those between the blocks of days from each month.

Thus for each month there are (m-n-KL) contiguous intervals, where m is the

length of the month and n is the length of the interval that is used to

represent the month. In this research work, an interval length of four days

has been used. The program has been written so that the length of the interval

is a variable that is input by the user. This interval length of four days

has proven adequate for light weight buildings, and gradually becomes less

accurate with increasing building mass.

Several criteria were identified in determining the nature of the algorithm

for selecting the appropriate intervals. First, the algorithm should be biased

as little as possible toward any particular building type or climate. A short

c year adhering to this criterion could be created once and reused for many

buildings. In addition, the same algorithm could be used to create short years

7
for many climates. Second, the statistics used for selection should be based

as directly as possible on the effects of climate variables on building energy

usage. Third, the selection algorithm should be able to utilize characterizing

statistics not only from specialized analysis of a particular year of hourly

observations, but also from long-term published averages. These criteria allow

the creation of an abbreviated tape representative either of a particular year

at a site or of the long-term conditions at that site.

The algorithm developed to meet these criteria bases the selection on the deviation

of the interval averages from the monthly averages for the climate variables.

A single composite score for the interval is generated by summing the deviations

of the variables with each weighted, according to the impact of the variable on

energy usage in buildings. Several assumptions underlie this algorithm. The

most important of these is that heating and cooling requirements for a month

are approximately a linear function of the monthly averages of certain weather

statistics. The second assumption is that the importance of each climate

variable to heating and cooling requirements is of the same order of magnitude

for wide variety of buildings. This assumption can be restated as follows:

if the building envelope determined heating and cooling requirements can be

represented as a linear function of several climatic variables, then the ratio

of any two coefficients for the climate variables will be of the same order

of magnitude over a variety of buildings. Given these two assumptions, the

coefficients of the linear model become the weights for computing the composite

score.

A final consideration in developing this technique was that during temperate

months, heating and cooling requirements are a function of excursions from the

mean temperature rather than the mean temperature itself. The temperature range

8
over the interval, however, is not an appropriate component for a linear model

and therefore does not lend itself to the composite scoring methodology. In

order to insure that the interval both conforms to the means and has wide

variation, the algorithm selects the Interval with the greatest temperature

range from among those intervals with composite scores below a set maximum value.

The algorithm may be tuned to account for the relative importance of the climatic

mean and temperature range by adjusting this maximum score.

In order to implement the climate data abbreviation technique outlined above,

one must determine the importance of various climate variables to building

energy usage. The measure of this importance would be a set of numerical

weights by which the deviations of the climate variables between the interval

and the month can be converted to a single score. Variations in geometry,

orientation of glazing, internal heat sources, and operating conditions cause

each building to respond uniquely to changes in climate variables. To create

weights typical of a wide variety of buildings, a testing program was set

up to determine the range of responses of different buildings to climate variables

in several geographic locations, for both heating and cooling.

Several assumptions, made previous to the design of the testing program, allowed

the scope and effort to be limited. The abbreviation technique was assumed

to be applicable to moderately well insulated building with skin dominated loads.

It was also assumed to be applicable only to locations in the continental

United States. Accordingly, three building configurations, a light building,

a heavy building, and a solar intensive building, were selected and were simulated

using TRY climate tapes in three geographic locations. [5] Calculated heating

and cooling requirements were then subjected to least squares regression analysis

with climate variables. The range of variation of these normalized coefficients,

9
their calculated significance levels, and goodness-of-f it considerations could

I determine which variables were important and whether a single set of weights

were justifiable. Specifically, one could examine the assumptions of linearity

and limited variation mentioned under the previous heading.

The three residential building types were selected to maximize the variation in

their response to climate variables. The first type is a compact ’ranch style'

house representative of today’s construction practices. [6] The 1200 square foot

(111.5 m ) house has 55 square feet (5.1 m ) of window facing north and 72

O square feet (6.7 m ) facing to the south. Figure 1 shows plans, elevations and

2 —

1

a section. The walls are insulated to R-ll (1.94 m C W ) and the attic to

2 —1

R—19 (3.34 m °C W ). Single glazing is used in all windows. The base rate

for infiltration is assumed to be 1/2 air change per hour, and varies with the

wind and the temperature differences between interior and exterior. The second

house is a passive solar variant of the ranch house. The south glazing is increased

O to 140 square feet (13.0 m ) and is protected by a 4.5 ft (1.37 m) foot overhang.

Wall insulation is raised to R-15 (2.64 m C W ) and double glazing is used.

The four inch (.10 m) slab and carpet are replaced with a six inch (.15 m) slab

with tile which is laid over two inches (.051 m) of polystyrene foam insulation.

North facing glazing is reduced to 40 square feet (3.72 m ). Plans and sections

of the building are shown in Figure 2. The third building is a massive infill

building with party walls oriented north and south. The party walls are constructed of 8-inch (.20 m) concrete with dry wall finish. The roof is constructed

of precast plank with lightweight concrete insulation. The east and west walls

are of brick and block construction with 100 (23 m ) square feet of glazing

in each face (Figure 3). The variations in orientations, mass, insulation level

10
and glazing area represent a wide range of construction practice and constitute

a good test for variation of response to climate variables. The schedules of

internal heat sources in each house was based on occupancy by a family of four.

The three climates chosen, Washington, D.C., Phoenix, Arizona and Minneapolis,

Minnesota encompass most of the variation found within the continental United

States. Washington is a temperate coastal climate which is humid in the summer.

Minneapolis is a cold continental climate, while Phoenix is a hot arid continental

climate. The climates represent variations, not only in temperature and humidity,

but in daily temperature range, wind speed, and cloud cover.

The initial set of simulations were calculated using thermostat settings of

68°F (20°C) for heating and 78°F (25.6°C) for cooling. These settings may

be considered prudent standard practice. Each house was simulated in each

location using the NBSLD program. [7] Heating and cooling requirements were summed

on a daily basis and were subjected to regression analysis using temperature,

wind speed, cloud cover and daily range as independent variables. Analysis

of the regression results revealed several complications which necessitated

a different approach to both the simulation and the regression. A graph of

daily heating and cooling requirements against daily mean temperature for the

passive variant in Washington, D.C. (Figure 4) reveals the primary complication:

the relationship is not linear. The dead zone between 68°F (20°C) and 78°F

(25.6°C), in which temperature changes have no impact on heating and cooling

requirements, creates a discontinuity. A second complication occurred on days

in which both heating and cooling requirements were calculated. The thermal

behavior of the buildings on these days could not successfully be represented

as a function of daily averages of climate variables.

11
According to theory, heat transfer between the interior and exterior of the

building is a continuous function of temperature differentials. The discontinuity

shown in Figure 4 is caused by the effect of the thermostat settings on

the conversion of thermal gain or loss to heating or cooling requirements.

The nonlinearities in heating and cooling requirements caused by changeover

from heating to cooling or by thermostat deadzone prevent the calculation of

a suitable regression model. A thermostat strategy was therefore sought that

would result in the most straightforward conversion of thermal gain or loss

to heating or cooling requirements. For the second set of simulations, each

building in each location was run twice; once with a low thermostat setting

resulting in cooling requirements only throughout the entire year, and once

with a high setting so that heating requirements only were calculated. The

thermostat setting used were 112°F (38.9°C) and 22°F (-5.6°C). The results

of using the two thermostat settings can be compared to determine how the

behavior of the building differed for heating and cooling. Conversion of

heat gain or loss to heating and cooling requirements is almost independent

of climate and therefore, coefficients developed for a constant thermostat

setting should be valid for selecting climate samples for use with varying

thermostat schedules.

Some experimenting was also required to identify suitable climate variables for

the selection algorithm. Those variables initially thought to be important

were temperature, solar radiation, wind, daily temperature range, and humidity.

Solar radiation values are not, however, included in the TRY format tapes.

Solar radiation values needed for NBSLD are constructed from the cloud cover

values on the tape using the algorithm developed by Liu and Jordan. [8] It

was decided, for the following reasons, to use cloud cover as an independent

12
variable, rather than reconstructing solar radiation values. Construction of

solar radiation values and summation for a day requires integration of the

radiation on a specifically oriented surface. By selecting such a surface,

one might cause a geometric bias in the independent variable. Cloud cover

represents solar radiation on the building without being specific to any

geometry. Also, long-term monthly averages for cloud cover are available for

most locations, while those for solar radiation are not.

Regressions were run on the daily sums and averages for both the entire year

and each month in order to check the linearity of the model. Ideally, the

coefficients calculated for each month should be similar to one another and

to those calculated for the entire year. The coefficients for temperature

exhibited this similarity, while those for cloud cover and wind did not. For

some of the buildings, the coefficient for cloud cover exhibited a change of

sign from summer to winter. The coefficient for wind speed became negligible

during winter for the cooling simulations and during summer for the heating

simulations

.

Analysis of these results suggested some changes for both dependent and independent variables. The change in sign for the cloud cover coefficient was most

dramatic in the cooling simulation of all three buildings in Washington, D.C.

The probable cause of this anomaly was the high correlation between cloud cover

and relative humidity. The summer coefficients for cloud cover for the Washington,

D.C. cooling simulations were correlated to high latent loads. For the ranch

and passive houses, which have no east-west windows, the increase in solar

load correlated to the reduction in cloud cover was overwhelmed by the decrease

in latent load also correlated with the cloud cover reduction. This dilemma

could be solved only by splitting the dependent variable, total load, into

13
two variables: latent load and sensible load. Note that neither the calculation of solar radiation values from the cloud cover parameter, nor the

use of actual radiation values, would have eliminated the correlation between

sunlight and humidity that caused the unstable coefficients. The regression

therefore was performed using sensible load as the dependent variable, and the

change of sign in the cloud cover coefficient was eliminated. Cloud cover coefficients for each month varied across the year. Each building type, however,

varied similarly in all three locations indicating that the probable cause was

the shifting geometry of the sun to the building.

The change of the wind coefficient suggested that a temperature-wind product

might be a more indicative variable. The use of such a product for modeling

the thermal performance of buildings had been suggested in the Twin Rivers study

at Princeton. [9] The regression with this product was occasionally unstable

with mutually cancelling variations between the coefficients calculated for the

two variables. Interestingly, the locations themselves demonstrated a specific

pattern for this phenomenon over all three houses, indicating that the variations

were a function of the particular weather rather than the building. Changing

the variable from the product of the average daily temperature and the average

daily wind speed to the daily average of the hourly wind-speed temperature product

eliminated this problem and permitted a stable coefficient to be obtained.

The climatic variable most indicative of the latent load was discovered in a

slightly different way. By observing months in which the interior-exterior

temperature difference and the exterior relative humidity result in a direct

conversion of latent gain or loss to requirements for humidity adjustment (instead

of merely acceptable changes in the interior relative humidity), it was found

14
that most of the variation of the calculated latent load is a function of the

change in the moisture ratio of the exterior air.

The coefficient for this variable developed by regression ranged between 6480 and

11400 (Btu *ft.^)/(hr *lb) [427 and 752 (kJ *m^)/hr *kg] . Latent gains or losses

by infiltration are calculated directly in the NBSLD program as a function of

moisture ratio and infiltration-rate. By using the equation from NBSLD and an

infiltration rate of .5 air changes per hour, a coefficient of 9158.4 (604)

was calculated for the average volume of the three houses. As this value was

near the mean of the coefficients developed by regression, it is used in the

selection algorithm. The moisture ratio may be calculated directly from dry-bulb

and dew point temperatures using ASHRAE approved algorithms. [2]

Table 1 gives normalized values for the coefficients calculated for sensible loads

in each of the houses in each location in both heating and cooling modes. The

relative importance of the climate variables is shown not to vary much among

the three building types in all climates. This lack of variation supports the

second underlying assumption of the method, so that the abbreviation technique

is valid for a wide range of buildings.

The generalized weights for use in the abbreviation technique were taken from

the mean value of the annual raw coefficients for each climate variable.

The final equation for the composite score is:

Score = 7 . 5 | ADB | + 4.7|ACC| + . 1 | AWSDB | + 9158.4|AWA|

15
where ADB deviation of dry bulb temperature (°F)

ACC deviation of cloud cover (tenth of cover)

AWSDB deviation of wind speed drybulb temperature product

AWA

(°F Miles hr.

deviation of moisture ratio lb/ft

5. IMPLEMENTATION OF THE ALGORITHM

A computer program was written incorporating the abbreviation technique outlined

previously and the findings of the investigation of climate variables. A listing

of the program is given in Appendix A. In creating an abbreviated version of

a TRY tape, the program first calculates average values for each month for the

following variables: temperature, cloud cover, wind-temperature product, and

absolute humidity. The program then calculates the averages of these variables

for each contiguous four day interval in the month. The deviations between

the averages for the interval and the month are weighted and combined to a single

score for each interval with the lowest score being the best. From those intervals

in the lowest 15% of all scores, that interval with the largest range of temperatures

is selected. This range was arrived at by a process of tuning; comparing the

results of the short year and full year simulations for the three buildings

in the three locations.

For abbreviation of a TRY tape by correction to long-term averages, monthly longterm averages from NOAA for dry-bulb temperature, cloud cover, wind speed, and

dew point temperature are input. Algorithms convert these variables to those

given above to allow the user to compare them to the monthly averages found

on the tape, and the program proceeds as before, comparing the intervals to

long-term monthly averages.

16
The short year for use with the NBSLD program consists of twelve intervals that

represent the twelve months of the year, plus an initial four days of data for

initialization purposes. Because the NBSLD program is a transfer function

program, it bases its calculations not only on the conditions for the current

hour of calculation but also on a history of conditions. Thus the simulation

must be run for a number of iterations to allow the initialization errors to

I be purged. The initial four day run period, which consists of four repetitions

of January 1 from the tape, allow this "warming up" process to occur.

6. ANALYSIS OF RESULTS

The first test performed was the verification of daily mean temperature as one

of the climate variables, instead of the more precise degree-hour measure. A

recent paper demonstrated that the use of the degree-hour resulted in better

characterization of climate than daily mean temperature. [10] In order to test

the importance of this distinction, a version of the abbreviation program was

created that employed degree hours instead of daily mean temperature. The weight

used to combine the deviation between the interval and the month for this variable

was merely the weight for temperature divided by 24. The base temperatures used

to calculate heating degree-hours was 55°F (12.8°C), and for cooling degreehours, 68°F (20°C). When this program was run for six TRY sites, very little

change was noted from the program utilizing daily mean temperature. In fact,

for every month tested, the interval obtaining the best composite score using

daily mean temperature also obtained the best score using degree-hours. The

results of this test indicate that daily mean temperature is an adequate measure

for abbreviation purposes. The use of mean temperature has the advantage of

allowing modification of the short year to long term means, because long term

mean temperatures are published while degree-hours are not.

17
The abbreviation technique was applied to six TRY climate tapes for the initial

testing. In addition to the sites for which climate variables were tested,

Washington, D.C., Minneapolis, Minnesota and Phoenix, Arizona, three additional

sites, Miami, Florida; Columbia, Missouri and Boston, Massachusetts were added.

These three sites were chosen for their diversity, representing respectively a

coastal warm climate, a continental temperate climate, and a coastal cool

climate. Full year analyses were run for each of the three building types

in each of the six climates, and were compared with respective short year analyses.

The results of this comparison can be seen in Figures 5 through 22.

On the basis of the yearly summations, the correspondence between the short year

and full year results is quite good. For heating, the correlation coefficient

between the two results for the 18 examples is .9987 and for cooling the coefficient

is .9978. The RMS error for heating is 89.4 x 10^ Btu (94.32 x 10 7 j) with

4 7 the mean heating value of 1751.6 x 10 Btu (110.99 x 10 j) . For cooling the

error is 105.2 x 10^ Btu (110.99 x 10 7 j) for a mean of 1617 x 10^ Btu (1708.05

x 10 7 j). The RMS error for the sum of heating and cooling requirements over

the 18 examples is 119.4 x 10^ Btu (125.97 x 10 7 j).

Closer inspection of the results gives some insight into the usage and limitations

of the short years. Both the passive and the townhouses suffer to some extent

from errors caused by the drastically increased rate of seasonal change in the

short years. A good example is the month of November for the Washington townhouse.

Because each month is represented by only four days, the heating of the massive

masonry and concrete townhouse during the eight days representing August and

September could continue to significantly affect the response of the house as

many as eight days later. This house, however, represents as massive a building

as might normally be encountered (the roof section has 48 significant transfer

18
functions and a common ratio of .9226). The magnitude of this error can be

seen by comparing the totals for the interval from the short year analysis to

the totals calculated for that four days extracted from the full year analysis.

For the example given above, the actual calculated heating requirement for the

four days representing November from the short year, before multiplication by

4 n 7„5 to represent the entire month, is 2,92 x 10 Btu (3.08 x 10' J). The calculated heating requirement for those four days from the full year analysis is

14.03 x 10^ Btu (14.80 x 10^ J). The difference between these two calculations

is that in the short year the interval is preceded by four days from October,

four from September, and so forth. The interval from the full year is preceded by other November days. Despite the magnitude of this error for the month

of November, the total error for the yearly summation is -8.8%.

Another method of looking at the results of this analysis is to examine the

ability of the short year to predict the differences in heating and cooling

requirements between the three building types. Results of this examination are

shown in Table 2.

The figures given are for the sum of heating and cooling requirements for each

house in each location. The addition of the heating and cooling requirements

is a reasonable representation of the total energy required for conditioning the

house assuming fossil fuel heating and electric air conditioning. The short year

predictions of savings in the modification of the ranch house to the passive

house are uniformly good, with the possible exception of Phoenix, for which the

difference between passive and ranch is less than the precision of the short

year method as demonstrated by the RMS error for the group. The difference between

c the ranch and townhouses was also well predicted by the short years. Once again

19
Phoenix is the worse case, but the actual difference between the two buildings

is less than 3% of the total. The calculated differences between the townhouse

and passive houses are not well represented by the short year. While in no

case does the short year prediction reverse the sign of the difference, its

magnitude is often significantly in error. The high percentage error in predicting

the differences between the various townhouses and the passive houses is caused

both by the small magnitude of the differences between these buildings as compared

to the differences between the ranch houses and the others, and by the somewhat

decreased accuracy of the four-day intervals in predicting the energy requirements

of these more massive buildings.

A second set of tests were performed to determine the effectiveness of the short

years for predicting the results of varying a single parameter in a building.

For this purpose, Fort Meyer Building 219, for which the NBSLD input stream

has been published, was chosen. [7] This building is an office of 3265 square

feet (303.3 m^) with 13 inch (.33 m) uninsulated brick walls, an Rll (.194 m^

°C W ) ceiling below an attic, 155 square feet (14.4 m ) of glass facing west,

2 and 80 square feet (7.4 m ) facing east. Heating and cooling requirements for

this building were calculated with both long and short years for eight orientations.

Thus for each run, the building description indicated a rotation of 45° from

the previous analysis. The test was run using TRY tape for Washington, D.C.

and the short year tape extracted from it. The calculated results for both

long and short years are shown in Figure 23. The RMS error for heating requirements calculated by the short year method is 4.72 x 10^ Btu (4.98 x 10^ J).

For cooling the error is 1.58 x 106 Btu (1.67 x 109 J). The magnitude of this

20
error, however, does not reflect the excellence with which the short year predicts

the ordering of the long year results. A better measure of correspondence would

be the residual standard deviation of a simple regression of the long versus

short year heating or cooling requirements using the long year calculation as

the dependent variable. The results of the regression for both heating and

cooling are shown in Table 3. The correlation coefficients for heating and

cooling are .989 and .990. While the slope coefficients and the intercept show

the deviation of the short year heating and cooling requirements from the full

year, the low residual standard deviation and the high correlation coefficient

show the highly linear relationship between the two sets of results. This linear

relationship indicates that prioritization of energy conserving design changes

could confidently be performed using the short year method of analysis.

Although the coefficients were determined with constant thermostat settings, they

appear to be valid for both setbacks and "dead zone" thermostat strategies.

7. CONCLUSION

This report presents a climate data abbreviation technique for reducing the computation and analysis costs of predicting building energy use. The cost reduction

should encourage designers to perform more iterative building optimization runs

and thereby design more cost effective buildings.

The influence of each climatic variable (temperature, humidity, sun, wind) on

building energy use was analyzed by regression from a data base of the energy

use of three diverse buildings calculated hourly for three locations across the

U.S. These influences are expressed in the form of coefficients in a linear equation

c used to select short intervals of real weather data from one [or more] full year

weather tapes. The short intervals are chosen to represent the longer periods

21
(in this case months) with a minimum difference in predicted energy consumption.

The selection algorithm was tested for accuracy by comparing short versus

long year heating and cooling calculations for three buildings in six locations,

and by using a different building exposed to eight different orientations. The

standard error of the short year predictions of the full year calculated heating

and cooling requirements is about 3.5 percent of the mean of the total requirements.

Variations on monthly basis were wider because of the small sample size. Months

with both heating and cooling requirements show the widest percent variations,

but the energy contribution of these months to the annual total is of course small.

The short year was very successful in predicting the relative energy demands

of the different building orientations. This test suggests that the climate

abbreviation technique is effective in comparative optimization studies.

Some deviation was noticed in the monthly predictions for the more massive

townhouse. Further study is needed to determine the optimum interval lengths

for predicting the response for massive buildings with significant thermal

lag. The program has been written so that the length of the interval is input

by the user.

Abbreviated year weather tapes may be selected using weather statistics

representing long term periods of record. In this way the abbreviated

year may represent the long term climate better than the source weather

data tape. The program offers the user the choice of inputting into the

selection algorithm summarized long term averages instead of the usual

selection criteria, which are computed from the weather data tape itself.

22
Studies are under way to determine whether this program can be used to

produce short year tapes which are characteristic of locations other than that

of the source weather tape. Currently, hourly weather tapes are available

for a limited number of first order weather stations. The abbreviation program

could be used to produce short tapes using averages for weather variables from

the far greater number of second order stations. The abbreviation program would

thereby extrapolate the data from the first order station to the regions

surrounding it.

The listing of the interactive selection program is presented in the appendix.

Notes

[1] Private communication with T. Kusuda.

[2] ASHRAE Task Group on Energy Requirements for Heating and Cooling,

"Algorithms for Building Heat Transfer Subroutines", New York, 1975.

[3] G.P. Mitalas, Calculation of Transient Heat Flow Through Walls

and Roofs , ASHRAE Transactions, Vol. 2 1968, p. 185.

[4] T. Kusuda, J.E. Hill, S.T. Liu, J.P. Barnett and J.W. Bean, Pre

Design Analysis of Energy Conservation Options for a Multi-Story

Demonstration Office Building , Building Science Series 78, National

Bureau of Standards: 1975.

[5] National Climatic Center 1976. Tape Reference Manual, Test Reference

Year, Asheville, N.C.

[6] S.R, Hastings, Three Proposed Typical House Designs for Energy

Conservation Research , NBSIR 77-1309, National Bureau of Standards,

1977.

23
[7] T. Kusuda, NBSLD, The Computer Program for Heating and Cooling Loads

in Buildings, Building Science Series 69, National Bureau of Standards, 1976.

[8] B.Y.H. Liu and R.C. Jordan, "The Interrelationship and Characteristic

Distribution of Direct, Diffuse and Total Solar Radiation," Solar

Energy , Vol. 4, No. 3, July 1960, pp. 1-19.

[9] R. Socolow and R. Sonderreger, Twin Rivers: A Five Year Summary Report ,

Center for Environmental Studies, Princeton University, Princeton, N.J.,

1976.

[10] L.S. Meyer and J. Benjami, 1977. "Modeling Residential Demand for Natural

Gas as a Function of the Coldness of the Month," Energy and Buildings ,

Vol. 1, No. 3.

[11] E. Dean and A. H. Rosenfield, "Modeling Natural Energy Flow in Houses,"

Energy and Buildings , Vol. 1, No. 1, Lausanne, Switzerland, 1977.

[12] L. Degelman, A Weather Simulation Model for Building Energy Analysis , ComputerAided Design and Simulation Laboratory, Pennsylvania State University,

University Park, Pennsylvania, 1974.

[13] D. Nall and E. Arens, The Influence of Degree Day Base Temperature on

Residential Building Energy Prediction, ASHRAE Transaction 1979 Vol. 1.

24
TABLE 1. Normalized Coefficients for Weighting Climate Variables

HEATING

Temperature Cloud Cover Wind Temperature Product

WASHINGTON, DC

Ranch .65 .33 .02

Passive .53 .46 .01

Town .56 .43 .01

MINNEAPOLIS, MN

Ranch .65 .34 .01

Passive .57 .42 .01

Town .57 .42 .01

PHOENIX, AZ

Ranch .69 .30 .01

Passive .56 .43 .01

Town .54

COOLING

.44 .02

WASHINGTON, DC

Ranch .75 .24 .01

Passive .60 .39 .01

Town .60 .39 .01

MINNEAPOLIS , MN

Ranch .68 .31 .01

Passive .57 .42 .01

Town .52 .47 .01

PHOENIX, AZ

Ranch .65 .34 .01

Passive .51 .47 .02

Town .57 .42 .01

Ave rage .60 .39 .01

25
TABLE 2 Long Versus Short year Prediction of Energy

Usgae Differences Between Buildings in Btu x 104

Ranch to Passive Town to

Ranch Passive Town Passive to Town Ranch

BOSTON

Long 3712.5 2202.2 2626.6 - 1510.3 424.4 1085.9

Short

Error (%)

3706.7 2119.4 2639.8 - 1587.3

5.1

520.4

22.6

1066.9

1.7

COLUMBIA

Long 3707.6 2539.6 2935.1 - 1168.0 395.5 772.5

Short

Error (%)

3563.3 2342.3 2818.1 - 1221.0

4.5

475.8

20.3

745.2

3.5

MINNEAPOLIS

Long 6095.8 4165.7 4705.1 - 1930.1 -539.4 1390.7

Short

Error (%)

5989.8 4268.9 4553.1 - 1720.1

- 10.8

-284.2

-47.3

1436.7

3.3

MIAMI

Long 3391.0 3837.4 3899.1 446.9 61.7 508.1

Short

Error (%)

3262.1 3683.1 3764.1 421.0

-5.7

81.0

31.3

502.0

- 1.2

PHOENIX

Long 3296.6 3187.6 3205.8 109.0 18.2 90.8

Short

Error (%)

3343.7 3193.3 3263.2 150.4

38.0

69.9

284.0

80.5

- 11.3

WASHINGTON

Long 2862.6 1973.5 2310.7 889.1 337.2 551.9

Short

Error (%)

2743.8 1791.9 2182.3 951.9

7.1

390.4

15.8

561.5

1.7

26
TABLE 3-A. Heating Regression Results

Fort Meyer Building

Least Squares Fit of Response (Short Year Results) as a Linear Function of

a Constant and an independent Variable (Long Year Results)

Estimates from Least Squares Fit

Indep Var.

Cons tant*

Long year Results

Residual Standard

Based on Degrees

Mean of Response

Coefficient

-9.989747 0

1.0445478

Deviation = of Freedom

S.D. of Coefficient

7.6137124

.064406681

.15556847

8 - 2 = 6

118.21

Ratio Accuracy*

-1.31 5.46

16.22 6.54

TABLE 3-B. Cooling Regression Results

Fort Meyer Building

Least Squares Fit of Response (Short Year Results) as a Linear Function of a Constant and an Independent Variable (Long Year Results)

Estimates from Least

Indep Var. Coefficient

Constant* -7.0866289

Long Year Results 1.1702253

Residual Standard Deviation = Based on Degrees of Freedom

Mean of Response =

Squares Fit

S.D. of Coefficient Ratio Accuracy*

2.2362117 -3.17 6.09

.068947535 16.97 6.82

.16491220

8 - 2 = 6

32.42

* The coefficient for the constant is the intercept for the linear model. The

The coefficient for the long year results as the slope for the linear models.

27
r

<y

o

o

28


Q

Figure 4: Daily Mean Temperature to Daily Heating and Cooling Requirements

31
LU

s:

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CD

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SCALED SHORT YEAR

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Figure 5. Washington Ranch House 32
1200

iioo l

1000

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600

500 _ |

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Figure 6. Washington Passive House

33
1200

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Figure 7. Washington Town House 34
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Figure 8. Minneapolis Ranch House 35

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Figure 9 Minneapolis Passive House

36
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700

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Figure 10. Minneapolis Town House

37
HEATING/COOLING

REQUIREMENTS

10

BTU

FULL YEAR

COOLING

HEATING

SCALED SHORT YEAR

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aHEA^ING

Figure 11 Phoenix Ranch House
FULL YEAR SCALED SHORT YEAR

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Figure 12 Phoenix Passive House

39
900

800

700

600

500

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300

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Figure 13 Phoenix Town House 40
1200

1100

1000

900

800

700

600

500

m

300

200

100

0

-100

-200

-300

-m

-500

-600

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Figure 14. Boston Ranch House

41
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Figure 15 Boston Passive House 42
1200

1100

1000

900

800

700

600

500

900

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Figure 17 Columbia Ranch House 44
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900

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Figure 19 Columbia Town House

46
900

800

700

600

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m

300

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300

400

-500

-600

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Figure 20. Miami Ranch House 47
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Figure 22 Miami Town House 49
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Figure

23.

Fort

Meyer

Building

Results:

Comparison

of

Long

and

Scaled

Short

Years
i2

3

4

5

6

7a

9

1 0

1 l 12

13

14

15

16

17

18

1 9

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

n

o

APPENDIX: Listing of Program to Select and Create

Abbreviated Weather Years.

* SELECT*

C MAIN ROUTINE OF A PROGRAM TO ABBREVIATE YEAR-LENGTH WEATHERTAPES

C BY SELECTING SHORT CONTIGUOUS INTERVALS OF BAYS TO REPRESENT EACH

C MONTH®

C

C USE LOGICAL UNIT 8 TO INPUT FULL-YEAR WEATHER FILE IN NBSLD FORMAT ® C USE LOGICAL UNIT 9 TO WRITE SHORT-YEAR WEATHER FILE.

C

C

C THE INTERVALS ARE PRINTED FOR EVALUATION ACCORDING TO THREE

C TYPES OF RANKING SYSTEMS!

C TYPED RANKS INTERVALS BASED ON THEIR DRY BULB

C AVERAGE TEMPERATURE ALONE®

C TYPE 1 RANKS INTERVALS BASED ON THE REGRESSION

C FORMULA®

C TYPE2 RANKS INTERVALS BASED ON THE FORMULA

C ADJUSTED FOR INTERVAL TOTAL DRY BULB

C TEMPERATURE RANGE.

C THE SHORT-YEAR FILE IS CREATED FROM TYPE2 ONLY IN THIS PROGRAM.

C

C

cC VARIABLES IN ORDER OF LISTING!

C

cc dltadb:

cC DLTADR!

C WSDBSP!

C CCSPAN!

C DBSPAN

!

C SRANGE!

C ND AY

!

C DA VG

C DR!

C TMAX!

C TMIN!

C CLOUD!

C DBMAX

I

C DBMIN!

C WSDBY

:

C WAY!

c

C RANK 1

!

C RANK2!

c ccady:

C CCADSP!

C DLCCAD!

c

c dlwsdb:

cc wasp:

c

DIFFERENCE IN DRY BULB AVERAGE TEMPERATURE BETWEEN INTERVAL

AND MONTH* F®

DIFFERENCE IN DRY BULB RANGE BETWEEN INTERVAL AND MONTH, F.

AVERAGE WINDSPEED-TEMPERATURE-PRODUCT OF INTERVAL, F X MPH.

AVERAGE CLOUD COVER FOR INTERVAL* TENTHS.

AVERAGE DRY BULB TEMPERATURE FOR INTERVAL* F.

DRY BULB TEMPERATURE RANGE OF INTERVAL* F.

NUMBER OF DAYS IN MONTH.

DAILY AVERAGE DRY BULB TEMPERATURE, F.

DAILY DRY BULB TEMPERATURE RANGE, F.

MONTHLY MAXIMUM DRY BULB TEMPERATURE® F.

MONTHLY MINIMUM DRY BULB TEMPERATURE, F.

DAILY CLOUD COVER* TENTHS.

DAILY MAXIMUM DRY BULB TEMPERATURE. F.

DAILY MINIMUM DRY BULB TEMPERATURE, F.

DAILY WINDSPEED-DRY-BULB-TEMPERATURE-PRODUCT, F X MPH.

SUM OF HOURLY MOISTURE RATIOS FOR DAY, LB H20 VAPOR PER

LB DRY AIR.

RANK OF INTERVAL ACCORDING TO FORMULA.

RANK ACCORDING TO GREATEST TEMPERATURE RANGE.

DAILY AVERAGE CLOUD COVER DURING SUNLIT HOURS, TENTHS.

AVERAGE SUNLIT CLOUD COVER FOR INTERVAL, TENTHS.

DIFFERENCE IN SUNLIT CLOUD COVER FOR INTERVAL AND MONTH,

TENTHS.

DIFFERENCE IN W I NDSPE ED-TEMPE RATURE-P RODUCT FOR INTERVAL,

F X MPH ® AVERAGE OF DAILY MOISTURE-RATIO-SUMS FOR INTERVAL, LB H20

VAPOR PER LB DRY AIR.

51
58 C dlwa:

59 C

60 c RANKO

:

61 c scaled:

62 c SCALE 1

:

63 c SC ALE2

1

64 c intval:

65 c io:

66 c 1 1

:

67 c 12 : 68 c DB2AVM

:

69 c TO c DP2AVM

:

71 c 72 c CCAD2M

:

73 c 74 c wsavm:

75 c 76 c pbT2m:

77 c 78 c iselct

:

79 c wa2m:

80 c 81 c drspan:

82 c numday

:

83 c i int:

84 c MONTH

:

85 c iday:

86 c avg : 87 c DAYMAX:

88 c daymin:

89 c cc: 90 c ccad:

91 c wa: 92 c wsdb:

93 c ilong:

94 c clavgm:

95 c dbavgm:

96 c dravgm:

97 c ccadm:

98 c wsdbm:

99 c wam:

100 c 101 c A2ED : 102 c A3ED:

1 03 c A4Ed:

1 04 c ased:

105 c nspan:

1 06 c dbx : 107 c drx: •

1 03 c ccx:

1 09 c CCADX :

1 10 c wsdbx:

1 1 1 c wax:

i 12 c spmax:

1 13 c spmin:

1 14 c factos:

1 15 c factor;

DIFFERENCE IN MOI STURE-RAT I O-SUM-AVER AGES BETWEEN INTERVAL

AND MONTH, LB H20 VAPOR PER LB DRY AIR,

INTERVAL RANK ACCORDING TO DRY BULB TEMPERATURE.

NORMALIZED RANK ACCORDING TO DRY BULB TEMPERATURE.

NORMALIZED RANK ACCORDING TO FORMULA.

NORMALIZED RANK ACCORDING TO TOTAL TEMPERATURE RANGE.

ORDER OF INTERVALS IN MONTH FROM SUBROUTINE ORDER.

ORDER OF INTERVALS IN MONTH FOR DRY BULB TEMPERATURE.

ORDER OF INTERVALS IN MONTH ACCORDING TO FORMULA.

ORDER OF INTERVALS IN MONTH ACCORDING TO TOTAL RANGE.

INPUT DRY BULB AVERAGE FOR BIASING SELECTION FROM LONGTERM MONTHLY AVERAGES. F.

INPUT DEWPOINT AVERAGE FOR BIASING SELECTION FROM LONGTERM MONTHLY AVERAGES® F.

INPUT SUNLIT CLOUD COVER AVERAGE FOR BIASING SELECTION FROM

LONG-TERM MONTHLY AVERAGES® TENTHS.

INPUT WINDSPEEO AVERAGE FOR BIASING SELECTION FROM LONGTERM MONTHLY AVERAGES® MPH.

INPUT BAROMETRIC PRESSURE AVERAGE FOR BIASING SELECTION

FROM LONG-TERM MONTHLY AVERAGES. IN. HG.

BEST INTERVAL FOR MONTH.

CALCULATED MO I STURE-RAT I O-SUM FOR BIASING SELECTION FROM

MONTHLY AVERAGES. LB H20 VAPOR PER LB DRY AIR.

AVERAGE DAILY RANGE FOR INTERVAL. F.

NUMBER OF DAYS IN YEAR.

LENGTH OF INTERVAL IN DAYS.

NUMBER OF MONTH.

DAY OF MONTH.

DRY BULB TEMPERATURE AVERAGE. F.

DAILY MAXIMUM TEMPERATURE. F.

DAILY MINIMUM TEMPERATURE® F.

CLOUD COVER, TENTHS.

SUNLIT CLOUD CLOUD COVER, TENTHS.

MOISTURE-RATIO-SUM, LB H20 PER LB DRV AIR.

WINDSPEEO DRY-BULB-TEMPERATURE-PRODUCT, F X MPH.

SWITCH FOR AVERAGE CHANGING MODE.

AVERAGE CLOUDINESS FOR MONTH, TENTHS,

AVERAGE DRY BULB TEMPERATURE FOR MONTH, F,

AVERAGE DAILY RANGE FOR MONTH® F.

AVERAGE SUNLIT CLOUD COVER FOR MONTH, TENTHS.

AVERAGE WIND-SPEED-TEMPERATURE-PRODUCT FOR MONTH, F X MPH.

AVERAGE MOISTURE-RATIO-SUM FOR MONTH, LB H20 VAPOR PER L8

DRY AIR.

SAVING VARIABLE FOR DBA VGM.

SAVING VARIABLE FOR CCADM.

SAVING VARIABLE FOR WSDBM.

SAVING VARIABLE FOR WAM.

NUMBER OF INTERVALS IN MONTH.

SUMMING VARIABLE FOR INTERVAL DRY BULB TEMPERATURE.

SUMMING VARIABLE FOR INTERVAL DAILY RANGE.

INTERVAL CLOUD COVER.

INTERVAL SUNLIT CLOUD COVER.

SUMMING VARIABLE FOR INTERVAL WIND-SPEED-TEMPERATURE-PRODUCT

SUMMING VARIABLE FOR INTERVAL MOISTURE-RATIO-SUM.

INTERVAL MAXIMUM TEMPERATURE.

INTERVAL MINIMUM TEMPERATURE.

NORMALIZING VARIABLE FOR TEMPERATURE SCORE.

NORMALIZING VARIABLE FOR FORMULA SCORE.

SUMMING VARIABLE FOR

SUMMING VARIABLE FOR

52
1 16

1 17

1 18

119

120

121

1 22

123

124

125

126

127

128

129

1 30

131

132

133

134

135

136

137

l 38

139

140

141

142

143

144

145

146

147

148

149

1 50

151

152

153

154

1 55

156

157

1 58

1 59

160

1 6 1 162

163

164

165

166

1 67

1 68

169

170

171

172

1 73

C FACTQ2 : NORMALIZING VARIABLE FOR TOTAL RANGE SCORE.

C ISFIL1 SWITCH FOR MAKING SHORT TAPE.

DIMENSION DLT ADB { 28 i , DLTADRC 28 ) , WSDBSP { 28) , DBSPAN { 20 ) . CCSPANI 28) . 2 SRANGEI 281 ,NDAY« 12) • DA VG ( 12. 31). DR (12. 31). TMAX( 12),TMIN(12).

3 CLOUD ( 12.31). DBMAX( 12.31),DBMIN(12,31), WSDBYU 2 .3 1 ) . WAY( 12.31 ) • 4 RANK 1 (28) ,RANK2( 28) . CCADYC 12.31 ) ,CCADSP(28 ) , DLCCADI 28 ) . DLWSDBC 28)

5 . WASP ( 28) • DLWAI28) .RANKO (28) . SCALE 0( 28) , SC ALE 1 (28) . SCALE2( 28)

.

6 I NT VAL ( 23 ) . I 0 ( 28) . I 1 ( 28 ) , I 2 ( 28 ) , DB2A VM ( 12) .DP2AVM( 12) ,CCAD2M( 12) , 7 WSAVM( 12) .PBT2M( 12) , ISELCTt 12) . WA2M( 12) .DRSPAN(28)

CALL PGSIZE ( 3 .64

)

PRINT 145

READ (5,120) NUMDAY

PRINT 150

READ (5,120) I I NT

DO 5 K=l, NUMDAY

CALL DAY ( MONTH . I DAY . AVG » DAY MAX » DA YM IN ,CC . CCAD .U/A.WSDB)

DBM AX ( MONTH » I DA Y ) =DAY MAX

DBMI N( MONTH . I DAY )=0AYMI

N

DA VG ( MONTH , I DAY )=AVG

DR ( MONTH. I D AY )=OAYMAX—DAYMI

N

CLOUDIMONTH. IDAY)=CC

CCAD Y ( MONTH . I DAY )=CCAD

W SOB Y (MONTH, IDAY)=WSDB

WAY (MONTH, I DAY) = WA

IF (ICAY.EQ.l) T MAX ( MONTH) =DAYMAX

IF (IDAY.EQ.l) TMI N( MONTH ) =DA YM I

N

TMAX( MONTH )=MAX( OAYMAX, TMAX ( MONTH)

)

TMI N ( MONT H)=MI N( DAYMI N» TMI NC MONTH)

)

5 NDAY (MONTH S=I DAY

PRINT 125

READ (5,120) I LONG

IF ( I LONG® NE . 1 ) GO TO 15

PRINT 130

PRINT 140

PRINT 135

DO 10 M0NTH=i,12

READ (5,120) DB2AVM ( MONTH ) . CC AD2M ( MONTH) , WSAVM { MONTH ) .DP2AVM

2 ( MONTH) .PBT2M(M0NTH)

CALL PSY 6 ( DB2AVMC MONTH) .DP2AVM4 MONTH) ,PBT2M( MONTH) , WA2MC MONTH)

)

10 CONTINUE

15 CONTINUE

DO 155 MONTH= 1,1

2

CLAVGM=Q.

DBAVGM=0.

ORAVGM-O

CCADM=0*

WSDBM=Q.

WAM=0.

L=NO AY ( MONTHS

DO 20 1=1,

L

DB A VGM = DB AVGM+DAVG ( MONTH. I

)

DRAVGM=DRAVGM+DR( MONTH, I)

CC ADM=CC ADM + CCAOY (MONTH. I ) WSDBM=WSOBM+ WSDBY( MONTH, I ) WAM=WAM+WAY (MONTH. I

)

20 CLAVGM=CLAVGM+CLOUO ( MONTH, I

)

DBAVGM=DBAVGM/L

DRA VGM=DR AVGM/L

53
1 74

175

176

1 77

178

179

1 80

181

182

1 83

184

185

186

1 37

1 88

1 89

190

191

192

193

194

l 95

1 96

197

198

199

200

20 1 202

203

204

205

206

207

208

209

210

21 1 2 12

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

A8SDRM=TMAX{ MONTH )-T MINI MONTH)

CLA VGM=CLAVGM/L

CCADM=CCADM/L

WSD8M=WSD8M/L

WAM=WAM/L

A2ED= DBA VGM

A3ED=CCAQM

A4EO= WSOBM

A5ED=WAM

IF (ILONG.NE.l) GO TO 25

DBA VGM=DB2A VM ( MONTH!

CCADM=CC AD2MC MONTH

>

WSDBM=WSAVM (MONTH)* (65- D8AVGMJ

WAM=WA2M( MONTH) *24

.

25 CONTINUE

PRINT 30

30 FORMAT (

9 1 •

2X • • MONTH* , 8X,* DBAVG* , 15X, « WSDBT* » l 5X * • RANGE 9 , 5X,

2 • WAAVG* • 6X» * CC AD * ,5X. 9 TYPED* ,5X. • TYPE 1

• .5X. • PRINTS* ) PRINT 35® MONTH, DBAVGM, WSD8M. A8SDRM, WAM.CCADM

35 FORMAT (15®* TAPE » ,F9 . 2® 1 OX, F 1 0 . 2 , 1 OX , 3F 1 0 .2

)

IF (ILONG.NE.l) PRINT 40

40 FORMAT (/)

IF (ILONG.EQ.l) PRINT 45, A2ED, A4ED.A5ED, A3ED

45 FORMAT (5X,* INPUT • »F9 „ 2 , 1 OX . F 1 0 . 2 , 20X , 2F 1 0 .2

)

NSPAN=NDAY ( MONTH) -I INT+

1

DO 55 1=1, NSPAN

DBX=0 * DRX=0.

ccx=o.

CC ADX= 0

.

WSDBX=0.

WAX=0

.

DO 50 J = 1 « 1 I NT

K= I + J — 1 D8X=DBX+DAVG( MONTH® K)

DRX=DRXFDR ( MONTH® K)

CCX=CCX+CLOUD ( MONTH , K

)

CCADX=CCADX-t-CCADY( MONTH, K)

WSD8X = W SD8X+ WSDBY ( MONTH » K

)

W AX=W AX-*- WAY ( MONTH, K)

IF (J.EQ.l) SPMAX=DBMAX( MONTH, K)

IF (J.EQ.l) SPMIN=D8MI N( MONTH, K)

SPMAX=MAX( SPMAX, DBM AX ( MONTH , K )

)

50 SPMI N=MI N ( SPM I N, DBM I N( MONTH »K )

)

DBSPAN( 1)=DBX/I I NT

DRSPANC I )=DRX/I INT

CCSPANC I )=CCX/I INT

CCADSP ( I )=CC ADX/I INT

WSDBSP ( I ) =WSD8X/ I INT

W A SP ( I ) = WAX/4

.

SRANGEC I )= SPMAX— SPMI

N

OLTAD8 ( I ) =DBSPAN( I

) —DBAVGM

DLT ADR ( I ) =SR ANGE ( I ) — ABSDRM

DLCCAD ( I )=CCADSP( I )-CCADM

DLWSDB( I)=WSDBSP( D-MSOBM

DLWAC I )=WASP( I )— WAM

RANK0 ( I ) = ABS ( OIL T ADB ( I ) i IF (I.EQ.l) F ACTOS=R ANKO ( I I

54
232

233

234

235

236

237

238

239

240

24 l 242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

55

60

65

70

75

80

85

90

95

100

105

110

1 1 5

120

125

130

135

140

145

150

155

F ACT0S=MAX (FACTOSj R ANKO ( I )

1

RANK 1(1 1 = 7.534 ABS(DLTADB( IJ)+4.644ABS( OLCCAOC I ) } + « 1 *ABS ( DLWSD8

2 C I )) >4.541 . 06*80. *ABS(DLWA( I )

)

IF (I.EQ.l) FACTOR=RANKl ( I

)

F ACTQR= MAX (FACTOR* RANK 1(1 J

1

R ANK2 ( I ) = A8S( DLTADR< I )

)

IF (I.EQ.l} FACT02=RANK2( I

}

FACT02=MAX(FACTG2.RANK2( I }

CONTINUE

PRINT 60

FORMAT ( 5X» ® INTERVAL 6 • 1 IX. 8 DELTADB • , 14X, 9 DLTWSDB 9 . 34 X ,

9 RANK • , 6X * 2 9 RANK* ,6X» • RANK* } DO 80 1=1. NSP AN

SC ALEO ( 11 = 100. *( 1 •— R ANKO ( I >/FACT0S)

SC ALE 1 ( I)=10Q.*( l.-RANKK I } /F ACTOR}

IF {SCAIEHI».GT.85.) GO TO 65

SCALE2 ( 11=0.

GO TO 70

SC ALE2( I )=100,*( 1.-RANK2I I ) /FACT02

}

CONTINUE

PRINT 75. I »OBSPAN( I ) »DLT AOB ( I ) . WSD3SP (I}.OLWSOB(I)« SRANGE ( I ) . 2 WASPC I ) ,CCADSP( I ) , SC ALEO ( I ) , SCALE 1 ( I 1 • SCALE2( 1

}

FORMAT ( I 10. 1 1F10.2)

CONTINUE

DO 85 1=1.28

I NTVAL ( I

) =

I

CALL ORDER ( SC ALEO » I NTVAL . NSP AN

)

DO 90 1=1,28

10(1 )=I NTVAL ( 1

}

I NTVAL ( I > =

I

CALL ORDER ( SCALE1 • I NTVAL. NSP AN)

DO 95 1=1,28

11(1 } = I NTVAL ( I

)

I NTVAL ( I }=I

CALL ORDER ( SC ALE2 , I NT VAL , NSP AN

)

DO 100 1=1,28

12(11 = 1 NTVAL C I

)

I NTVAL < I ) = I PRINT 115

DO 105 1=1, NSPAN

J=NSP AN— I +

1

PRINT 110. J. IOC I J . SCALEO ( I ) , I 1 ( I J • SCALE1 (I ) . I 2( I ) . SCALE2(

I

i

I SELCT( MONTH )=I 2( NSPAN}

IF ( SCALE2 ( NSPAN} « EQ. 0 J I SELCT( MONTH) = 1 1 ( NSPANi

PRINT 170 I SELCT( MONTH)

FORMAT ( I 10. 3( I 1 0.F10.2)

)

FORMAT ( • /ORDERED RANK I NG S' /5X, • ORDER 8 . 3X

,

9 INTERVAL 9 .4X, 9 TYPEO* . 2 3X» 9 INTERVAL* ,4X» 9 TYPE

1

9

, 3X .

* INTERVAL* ,4X, *TYPE2*

)

FORMAT (

J

FORMAT {* DO YOU WANT TO CHANGE AVERAGES? 1=YES, RETURN=NO *)

FORMAT (• SEE CLIMATIC ATLAS OF THE UNITED STATES FOR AVERAGES 9 ) FORMAT (* TEMPERATURE COVER AVERAGE TEMPERATURE 9

, 2 * PRESSURE * ) FORMAT (• DRY BULB MEAN SKY WIND SPEED DEW POINT 9 , 2 'BAROMETRIC 9

) FORMAT {• HOW LONG IS THIS WEATHER TAPE IN DAYS?*)

FORMAT (* HOW MANY DAYS IN INTERVAL FOR MONTH*)

CONTINUE

55
290

291

292

293

294

295

296

297

298

299

300

12

3

4

5

6

7

a9

10

i 1 12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

PRINT 160

160 FORMAT (

* 1

9

) PRINT 165

165 FORMAT {» DO YOU WANT TO MAKE SHORT YEAR FILE? 1=YES. RETURN=NQ 0 > 170 FORMAT {• THE CHOSEN INTERVAL STARTS ON DAY *,I2)

READ (5,1201 ISFILE

IF < ISFILE. EQ. 1) CALL SHORTY UINT.ISELCT)

PRINT 690 ISFILE

690 FORMAT (12, » 1 MEANS SHORT YEAR FILE COMPLETE 9

1 STOP

END

C

cc

cc

c

cc

cc

cc

SUBROUTINE TO CREATE A SHORT WEATHER FILE GIVEN SELECTED INTERVALS.

variables:

NDAYS:

mowarm;

aleap:

ileap:

bleap:

inday:

n i

N2:

NUMBER OF DAYS IN EACH MONTH OF YEAR.

WARM—UP MONTH NUMBER (NEEDED FOR LOADS PROGRAMS) REAL QUOTIENT OF YEAR DIVIDED BY FOUR.

INTEGER QUOTIENT OF YEAR OIVIDEO BY FOUR.

REAL NUMBER EQUL TO ILEAP.

NUMBER OF DAY BEING DONE.

FIRST DAY OF INTERVAL.

LAST DAY OF INTERVAL.

SUBROUTINE SHORTY ( I INT » I SELCT)

DIMENSION ARRAY(2 16) , ISELCT( 12)

DIMENSION NDAYSC 121 /3I ,28,31,30*31 ,30,31 ,31 ,30 ,31 ,30,31/ M0WAPM=2

REWIND 8

ALEAP= I YEAR/4.

ILEAP=ALEAP

BLEAP=ILEAP

I F( ALEAP. EQ. BLEAP ) NDAYS(2J=29

10 READ ( 8 ) ARRAY. I YEAR, MONTH, IDAY, ICITY

I F ( MONTH oNE . 1 .OR. IDAY .NE. 1) GOTO IS

DO 12 J= 1 , I I NT

I NOA Y=3 1 — I INT-#-J

WRIT.EC 9) ARRAY, I Y EAR, MOWARM, I NDAY, I CITY

12 CONTINUE

15 Nl= I SELCT ( MONTH

)

N2=N 1 +1 I NT—

1

I ND AY— NDAY S( MONTH )-N2 FI DAY

IF( IDAY.GE.N1 .AND. I DA Y . LE . N2 ) WR I TEi 9 ) ARRAY , I YEAR, MONTH, I NDAY. ICITY

IF< IDAY. EQ.N2. AND. MONTH. EQ. 12) STOP

GO TO 10

END

56
12

3

4

5

6

7

8

9

1 0

1 1 12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

4 1 42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

C

Cc

c

cc

c

cc

cc

ccc

c

ccc

cc

ccc

c

c

cc

c

c

c

SUBROUTINE TO READ A TRY TAPE RECORD AND CALCULATE DAILY

AVERAGES FOR SOME OF THE WEATHER VARIABLES THEREIN OR CALCULATED

FROM THESE VARIABLES. NO INPUTS.

OUTPUT VARIABLES!

MONTH! NUMBER OF MONTH.

IDAV: NUMBER OF DAY OF MONTH®

A VG ! DRY BULB AVERAGE TEMPERETURE FOR DAY.

DAYMAX! MAXIMUM DRY BULB TEMPERATURE FOR DAY, F.

DAYMIN: MINIMUM DRY BULB TEMPERATURE FOR DAY, F«

CLOUD: CLOUD COVER AVERAGE FOR DAY, TENTHS.

CCADJ SUNLIT CLOUD COVER AVERAGE FOR DAY, TENTHS.

WAAVGS MOISTURE-RATIO-SUM FOR DAY, LB H20 VAPOR PER LB DRY AIR.

WSAVG: WINDSPEED-DRY8ULB-TEMPERATURE-PR0DUCT AVERAGE FOR DAY,

F X MPH.

variables:

DAWN: HOUR OF DAWN IN MONTH.

ARRAY: ARRAY FOR ACCEPTING 'TRY 9 DATA.

DB; HOURLY DRY BULB TEMPERATURE® F.

CC: HOURLY CLOUD COVER, TENTHS.

length: hours OF sunlight for day in month,

dpt: hourly DEW point temperature, f.

pbt: hourly BAROMETRIC pressure, IN. HG.

WST: HOURLY WIND SPEED, MPH.

iyear: year of tape.

total: dry bulb temperature summing variable.

TOC: TYPE OF CLOUD.

WA: MOISTURE RATIO, LB H20 vapor per lb dry air.

SUBROUTINE DAY ( MONTH, I DAY , AVG , DAY MAX , DA YM I N , CLOUD , CC AD , WA A VG , awsavg

)

INTEGER DAWN

DIMENSION ARRAY C 2 1 6 ) , DB{24), CC«24) .LENGTH ( 12 ) » DAWNI 12) , TOC( 24

)

A.DPTC 24) ,P8T (24) , WST 124)

DAWN, LENGTH DATA IS FOR 36 DEGREES NORTH LATITUDE

DAT A DAWN/8 ,7, 7,6, 6, 5,5, 6, 6, 7,7,8/

DATA LENGTH/7,9,9. 11,11.13, 13,11, 11, 9, 9, 7/

RE ADI 8, END=6 0 ) ARRAY, IYEAR, MONTH ,

1

DAY • I Cl TY

WA A VG=0

.

W S A VG— 0 » CLOUD=0.

TOTAL=0.

CC AD=0

.

DO 40 J= 1 » 24

DBI J)=ARRAYI j

)

DPT ( J 1= ARRAY I J+241

PBT ( J J = ARRAY C J + 96

)

IF (DPT<U)-DB< J) ) 20

DPT ( J }=DB ( J

I

20 PV=PVSF<DPTC J }

1

WA = 0.622*PV/I PBT i J) —PV i WST ( J)=ARRAY< JF72I

CC ( J ) = ARRAY I J-F 1 20 J T0C( J)= ARRAY ( J +144)

TQTAL=TOT AL+ DB (

J

) IFITOCIJU 35. .35

CCC JJ=.5*CC< JJ

57
58 35 CL OUD=CLQUD+CC ( J

I

59 WAAVG=.WAAVG + WA

60 WSAVG=taSAVG+WST { J) #( 65-DO ( J)

)

61 IFIJ.EQ.l) DAYM AX=DB< J

)

62 IFIJ.EG.l) DAY M I N=DB ( J

)

63 DAYMAX=MAX( DAY MAX.DBIJ)

)

64 40 DAYMIN=MIN< DAYMIN.DBI J)

}

65 CL 0UD=CL0UD/24 . 66 A VG=TOT AL/24 « 67 WSAVG=WSAVG/24»

68 I 1 =DAWN( MONTH)

69 I 2=1 1 -9-LENGTH ( MONTH)

70 DO 50 1=11*12

7 1 50 CC AD=CC AD+ C C I I

J

72 CC AD=CC AD/C LENGTH ( MONTH ) -9-1 ) 73 RETURN

74 60 RETURN

75 END

1 SUBROUTINE ORDER I TEMP* V * N

)

2 C ORDERS SCORES IN INCREASING VALUE AND ORDERS ARRAY

3 C

4 c inputs:

5 c temp: A SCORE TO BE ORDERED*

6 c v: ARRAY CONTAINING ORDINAL POSITION (IN INCREA

7 c OF ELEMENTS IN TEMP*

8 c n: NUMBER OF ELEMENTS TO BE ORDERED.

9 c outputs:

10 c temp: ORDER OF SCORES FROM SMALLEST TO LARGEST*

1 1 c 12 DIMENSION TEMPI 1 ) , V I 1 ) , AA ( 20 0 ) . AB I 200

)

13 DO 50 1 = 1.

N

14 BA= 1499.

15 DO 25 J= 1 » N

16 IF ( BA— TEMP { J ) ) 25.25.

1 7 K=J

18 BA=TEMPI J

)

19 25 CONTINUE

20 TEMPI K)=I500*

21 A A I I ) =8

A

22 50 A8< I ) = V ( K)

23 DO 75 L= 1 « N

24 TEMPIL )=AA(L)

25 75 V(L)=ABIL)

26 RETURN

27 END

ACCORDINGLY

ORDER)

58
12

3

4

5

6

7

8

9

10

1 1 12

13

14

15

16

17

18

12

3

4

5

6

7

8

9

10

1 1 12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

C PSYCHRGMETRIC SUBROUTINE FOR SHORT YEAR PROGRAM

C

C INPUT variables:

c db: dry bulb temperature, f.

c op: dew point temperature, f. c pb: barometric pressure, in. hg.

c output variables:

C w: MOISTURE RATIO, LB H20 vapor per lb dry air.

c pv: partial vapor pressure of water vapor in

C MOIST AIR, IN. hg.

c SUBROUTINE PSY6ID6, DP , PB, W

)

IF ( DP--DB l 20,10,10

10 QP=DB

20 P V = P V SF I DP 1 W = 0 .622*PV/I P8-PV

J

RETURN

END

C FUNCTION PSYCHROMETRIC SUBROUTINE FOR CALCULATION OF

C PARTIAL VAPOUR PRESSURE OF WATER AT A GIVEN TEMPERATURE

C FOR A SATURATED CONDITION.

C

C INPUTS X, A TEMPERATURE, AND RETURNS PVSF, THE SATURATED PARTIAL VAPOR

C PRESSURE OF WATER IN MOIST AIR.

C

FUNCTION PVSF(X)

DIMENSION A I 6 J /-7 . 90298, 5. 02808 , - 1 . 38 16E-7 . 1 l . 344

,

2 8. I 328E-3, -3.49149/ » B C 4 ) /— 9 • 09718 ,—3.56654 .0.876793, 0,0060273/

3 » P l 4 1 T= I X+459.688)/! .8

IFIT.LT. 273. 161 GOTO 10

Z=373. 16/T

PI 1 J = A{ t ( Z-l

)

PI2)=AI2)*L0G1QI Zi

Zl=AI4J4t l-l/ZJ

P(3)=A(3)*( 10*#Z1-1

J

Z1=AI6)*(Z-1)

PI 4)=A( 5)*( 10*#Z1-1

I

GOTO 20

10 Z=273 . 16/T

PI 1) = BI 1)*IZ-IJ

P(2)=BC2)*LOGlQ(Z»

P(3)=B(3)*< 1-1/ZJ

P I 4 ) =LOG 1 0 I B I 4 1

1

20 SUM=0

.

DO 30 1=1.4

30 S UM= SUM +P I I ) P VSF=29 .92141 0* 4 SUM

RETURN

END

59
FEDERAL INFORMATION PROCESSING STANDARD SOFTWARE SUMMARY

01. Summary date

Yr. Mo. Day

7 1 8 0 9 0 1

04. Software date

Yr. Mo. Day

— jj00 0 6 0 1

02. Summary prepared by (Name and Phone) Edward Arens (301) 921-3595

05. Software title SELECT: PROGRAM TO ABBREVIATE YEAR-LENGTH

WEATHER TAPES

06. Short title 08. Software type 09. Processing mode 10. Application General Automated Data Computer Systems Management/

| | System pt[ Interactive

| | Support/ Utility Business

pK| Computer Program Q Batch Scientific/Engineering Process Control

| | Subroutine/Module| ] Combination Bibliographic/Textual Other

03. Summary action New Replacement Deletion E

Previous Internal Software ID

07. Internai Software ID

Specific

11. Submitting organization and address Architectural Research Program

Environmental Design Research Division

Center for Building Technology

National Bureau of Standards

Washington, DC 20234

12. Technical contact(s) and phone

Edward Arens (301) 921-3595

13. Narrative Interactive program, plus five subroutines, abbreviates year-length weather tapes in

the format required for NBSLD by selecting short contiguous intervals of days to

represent each month. The selection algorithm was derived from regression of climate

variables against simulated loads of a variety of envelope-dominated buildings.

14. Keywords

Energy; climate data; buildings.

15. Computer manuf’r and model UNIVAC 1108

16. Computer operating system 17. Programing language(s) FORTRAN V

18. Number of source program state- ments 486

19. Computer memory requirements

100K

20. Tape drives None

21. Disk/Drum units L + S tracks, input,

output

22. Terminals

' 132 character/

line

23. Other operational requirements

; 24. Software availability

j 25. Documentation availability

Available Limited In-house only Available Inadequate In-house only 0 0

i

26. FOR SUBMITTING ORGANIZATION USE

185-101 Standard Form 185

1974 July U S. Dept, of Commerce—NBS

(FIPS. Pub. 30) 60
NBS-1 14A (REV. 1 1-77)

U.S. DEPT. OF COMM. BIBLIOGRAPHIC DATA

SHEET

1. PUBLICATION OR REPORT NO. NBSIR 78-1525

2. Gov’t Accession

No.

3. Recipient’s Accession No

4. TITLE AND SUBTITLE

Climate Data Abbreviation for the Computerized Calculation of

Heating and Cooling Requirements in Buildings

5. Publication Date December 1978

6. Performing Organization Code

7. AUTHORISE

Edward A. Arens and Daniel H. Nall

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OP STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

10. Project/Task/Work Unit No.

11. Contract/Grant No.

12. Sponsoring Organization Name and Complete Address (Street, City, State, ZIP) 13. Type of Report & Period Covered

14. Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

16. ABSTRACT (A 200-word or less factual summary ol most significant information. If document includes a significant

bibliography or literature survey, mention it here.) This paper documents the development of a climate data abbreviation technique for

building thermal analysis. The paper first discusses the characteristics of

computerized building thermal simulations and the requirements for abbreviated data.

The technique is then described together with the statistical analyses used to

develop it. A series of tests of the representativeness of the abbreviated climate

data are documented. Finally, the limitations and potentials of the abbreviation

technique are discussed.

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name; separated by semicolons)

Climate data; computer; energy; lcad-calculation; residential; weather

18. AVAILABILITY [jT Unlimited 19. SECURITY CLASS

(THIS REPORT)

;21. NO. OF PAGES

f1— For Official Distribution. Do Not Release to NTIS

C UNCL ASSIFIED

66

£

1 Order From Sup. of Doc., U.S. Government Printing Office Washineton. D.C. 20402. SD Stock No. SN003-003

20. SECURITY CLASS’

(THIS PAGE!

22. Price

|

i Order From National Technical Information Service (NTIS)

Springfield, Virginia 22151 UNCLASSIFIED $5.25

U S COMM- D C 8S03B-P78
r

- fl

.