An exploratory study in the development of a micro-computer assisted technique for community energy planning

Material Information

An exploratory study in the development of a micro-computer assisted technique for community energy planning
Pelton-Roby, Ruth
Publication Date:
Physical Description:
143 leaves in various foliations : ; 28 cm


Subjects / Keywords:
Energy conservation -- Data processing ( lcsh )
Energy consumption -- Data processing ( lcsh )
Energy conservation -- Data processing ( fast )
Energy consumption -- Data processing ( fast )
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )


Includes bibliographical references.
General Note:
Submitted in partial fulfillment of the requirements for a Master's degree in Planning and Community Development, College of Design and Planning.
Statement of Responsibility:
Ruth Pelton-Roby.

Record Information

Source Institution:
University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
08751113 ( OCLC )
LD1190.A78 1980 .P45 ( lcc )

Full Text
3 1204 00255 5754
auraria library
In the Development of A Micro-computer Assisted Technique For Community Ener ay P 1 annln a
Pel ton-Roby
August- 21
193 0

Table of Contents
0 . Introduction p 1
0.1 The Problem P 1
0.2 Thesis p 2
0.3 Organization p 4
0.4 Limits of the Study p 1
0.5 Cone 1us i on5 p 9
1 0 The Importance of Community Energy Planning p 1 1
1 1 World Energy Supply Background F 1 1
1 2 The Impact of the Energy Crisis on the Nation p 1 2
1 3 The Impact of the Energy Crisis on Comrnun i 11 e s p 1 3
1 4 Appropriateness of the Local Level
for Energy Planning P 1 0
1 5 Benefits of Energy Planning and
Conservation Programs p 1 7
1 6 Problems with Energy Planning p 1 7
2.0 Criteria for a Useful Community Energy
Planning Tool p 2 1
2 1 Local Applicability P a 2 1
r! / Comprehensive Analysis P 2 2
2.3 Within Local Means p 2 3
2.4 Time and Effort p 2 4
2.5 C o m p u t e r1z a tion p / 5
2.6 Assesses Alternatives P 76
2 7 Sumrnar y P 2 6
Existing Tools for Community Energy P l a n n l n s P 28
3.1 Brook haven Land Use and Energy Utilization Model p 29
3.2 Community Energy Planning, the Basis Elements'' p 30
3.3 "County Energy Guidebook" p 3 1
3.4 Hl111 ma n Methodology f 32
3.5 Linear- Programming Approach To County Level
Scenario Disaggregation" p 33
3.6 Macro-economic Models p 34
3 7 MATH/CHRDS" p 35
3.3 "OASIS" f 37
3.9 ORNL Engineering-Economic Model o f
Residential Use" f J8
3.10 "Planners' Energy Workbook" f 39
3.1 1 "Regional Energy Activity
and Demographic Model" p 3 9
3.12 "Regional Elnersy Demand forecasting Model " p 4 0
3.13 "SOLA RSIM" P 4 1
3. 14 "SPIJRR" P 42
3.15 3 ummar y p 4 2
Development of a Microcomputer Assi s ted Techni 'Hue
for Community Energy Planning p. 43
4 1 Selection of the Workbook for Computer nati on h 4 J

4.2 Selection of Hardware and Software
4.3 Adapting the "Community Energy Planning
Workbook to "Visi-calc" f.46
4.3.1 Single Family Residential f.46
4.3.2 Multi-family Residential f.50
4.3.3 Commercial p.51
4.3.4 Industrial p.53
4.3.5 Transportation f.57
4.3.6 Government f .53
4.3.7 Community Summary p 59
4.4 Selection of Community for Testing the Method p.60
4.4.1 Boulder p.6 o
4.4.2 Arvada p.61
5.0 Data Collection and Reliability p.62
5.1 Collecting the Input Data p.6 2
5.1.1 Residential p.62
5.1.2 Commercial p.64
5.1.3 Industrial p.70
5.1.4 Transportation p.71
5.1.5 Government p.72
5.1.6 Energy and Fuel Prices p.72
5.1.7 Summary of Data Collection p.74
5.2 Assumptions. Modifications, and Problems in Data Collection. Including Recommendations for Verification p.75
5.2.1 General Problems and Considerations p.75
5.2.2 Residential p.76
5.2.3 Cornme rc i a 1 P.7 7 Food Stores p.77 Eating and Drinking Places p 7 8 Retail and Services p 7 9 Hotels and Hotels f 8 0 Warehouses, etc. p 8 0
5.2.3. A Office Buildin3s p.81 Schools p.82 Value of Construction p.82
5.2.3. '? Miscellaneous Commercial p.32
5.2.4 Industrial p.8 3
5.2.5 Transportation f.85
5.2.6 Government p.86
5.2.7 Fuel Prices p.87
6.0 Testing the Method p.3'?
6.1 Function of the Visi-calc" Program and Error
in the Worksheet Format p 8'?
6.2 First Time User Experience p 9 0
6.3 Using the Technique to Project
Future Energy Use p.'?l
6.3.1 Growth Projection as a Simple Change p.91
6.3.2 Growth Projection as an Annual Rate p.93
6.4 Assessing the Impacts of Alternative
Conservation Measures f.94
6.4.1 Solar Water Heating p.95
6.4.2 Multi-family Residential p.95
6.4.3 Impacts on Industry p.96

6.4.4 Evaluation of Test Subject's Performance
6.5 Comparison of 0.E.C. Workbook Values to Boulder Eneray Study Results
6.5.1 Boulder Input Used with O.E.C. Analysis
6.5.2 Boulder and Arvada's Use Compared t .5.2.1 Residential Industrial Commercial
6.5.3 Evaluation of Eoulder/Arvada Comparisons
6.6 Computerization Compared to Hand Calculation
6.7 Summary
7.0 Evaluation and Recommendations
7.1 Evaluation in Terms of the Established Criteria
7.1.1 Local Applicability
7.1.2 Comprehensive Analysis
7.1.3 Within Local Means
7.1.4 Time and Effort 7 1 .5 Computerization
7.1.6 Assesses Alternatives
7.2 Recommendations for Improvement of This Technique
7.2.1 Improvements to the Audit- Technique
7.2.2 Improvements to the Computer Technique
7.3 Recommendations for Further Research
7.3.1 Eneray Audit Technique
7.3.2 Computerization
7.4 Conclusions
3.0 Biblioaraphy
9.0 Footnotes
p .97
p . V 7
p . 93
r . 9 9
P . 99
P . 1 0 i
P . 1 0 1
P . 102
F . 103
P . 105
P . 1 V 7
P . 10 7
P . 107
P 107
P . 1 08
P 109
P . 1 1 0
P . 1 1 1
P . 1 1 1
P . 1 1 2
P . 1 1 3
F . 1 1 3
P . 1 1 3
P . 1 1 4
P . 1 1 5
P 1 1 6
P . 1 23

0. Introduction
0.1 The Problem
The creation of OPEC and the oil ernbarso of 1973 marked the end of the era of cheap energy in the United States. Enersy prices have risen dramaticly in the past ten years and this trend is expected to continue.
Communities? as well as individuals? suffer from risins enersy prices and the threat of enersy shortaSes. The overall economic health of the community may be adversely affected. Residents are spendinS a Sreater proportion of their incomes pay ins their utility bills! this results in a decline in retail sales and therefore sales tax revenues. Enersy shortases and increased costs may cause industries to shut down? erodins the community's employment base.
Risins enersy costs resulted in an increased interest in enersy conservation techniques for use in homes? businesses? industry and sovernment. The concern with risins enersy costs also resulted? in the mid-'70's? in the development of the field of community enersy plannins.
Community enersy planning involves analyzing the current enersy use in a community and proiectins future needs. Enersy use is analyzed by type (electricity? natural
Sas? etc.) and by consuming sector (residential?

etc. ) .
Tarsets for conservation measures may
be identified.
As a result of the experience of pioneering efforts in community energy planning, several problems have been identified. These problems must be overcome if the
practice of community energy planning is to become widespread. First is the complexity and expense of
detailed community energy audits that have characterized the practice in the past. Second, there has been little integration of potential conservation techniques with the future demand projections. Thirdly, the data manipulation for community energy planning has typically occured at two extremes? either highly sophisticated computer models or the simple but tedious paper, pencil and hand calculator method. The problems with the former include expense and limited application. The problem with the later is that tedious calculations discourage staff from attempting the project in the first place, or from calculating various scenarios or alternate conservation statedies. The purpose of this project is to overcome at least some of those problems, specificly the data manipulation of the original audit, growth projections and conservation measures.
0.2 Thesis
What has been lacking in the field of community energy planning is a technique for estimating community energy use
and analyzing the effects of conservation measures which is

easier to use than the workbooks but which* unlike the
elaborate computer models* would have general application and accessabi1ity in terms of cost and staff resources required. Such a technique would not require extensive resources of hardware and computer programming support personnel. Instead* it would take advantage of readily available micro-computers and relatively inexpensive existing software. It should not require the assistance of a computer programmer to install or operate the technique. It should be capable of determining not only current energy use and projected future energy use based on current trends* but it should also be capable of showing the effects of energy conservation measures.
The technique will be based upon an existing community energy planning workbook technique developed for use at the local level. This method should be flexible enough to be used in a variety of situations* that is* communities of different sizes* in different locations. The data collection effort should not be excessive in its demands on staff time. The analysis should consider local conditions and should be reasonably accurate.
The intent is to develop a usable tool to assist in community energy planning. This tool will aid staff and elected officials in making decisions regarding conservation techniques and policy. It should be capable of identifying the vulnerable activities or institutions in a community* those which would be particulary harmed by an energy shortage or price increase and which would in turn

have a negative impact on the community as a whole if
energy supplies were curtailed.
0.3 OrSan i zat i on
In chapter one the subject of community eneray plannins is introduced with some background on the eneray crisis and its impact on the nation. The effects of risina enerdy prices and the threat that shortaaes have on communities are described. Support is aiven for selectina the community as the unit of analysis for eneray plannina. The benefits of eneray plannina are discussed* as are some of the problems encountered in the process. Chapter one is based on a review of the literature* as well as the author's personal experience in community eneray plannina.
In chapter two* criteria for a useful community eneray plannina tool are established. These criteria are based* primarily* on a review literature reaardins communities' experiences in eneray Plannina. The author has interjected her own professional opinion in one aspect of the criteria; that communities should be capable of perforrnina the eneray audit usina their own plannina staff? outside experts" should not be required.
Existina models and methods of eneray plannina are reviewed in chapter three. Those models with the areatest potential for community eneray plannina are included* as are several of the major workbook techniques. All are examined in terms of the criteria established in chapter

two. None is found to meet all the criteria, although several have interestins implications for energy planning.
In chapter four a proposal is made to develop a community energy planning technique which does meet all the established criteria. The most efficient and technicaly accurate means to do so is to computerize one of the work book techniques developed for use at the local level. To attempt to localize one of the existing large-scale computerized techniques would require engineering studies beyond the scope of this project. Justification is given for selection of the particular workbooks hardware and software chosen. The use of the Visi-ca 1 c software is explained briefly. The changes made while adapting the worksheets to computer files are described. Justification is given for selection of the test community.
The data collection process is described in detail in chapter five. Assumptions, data manipulation! and potential inaccuracies in the data are reported in this chapter as well.
The technique was tested in a variety of ways which are described in chapter six. First the function of the "Visi-calc" program was checked^ by performing the same calculations using a hand calculator. The format of the worksheet and accuracy of the formulas were checked for error. Several errors were found and corrected. Because the technique is supposed to be operable by persons not previously experienced with micro-computers, and because it should be useful for projecting future energy use and the

effects of conservation measures* a test subject was asked
to perform several tasks. The first was to learn the operation of the prosrarn and to enter the input data for the various sectors. The second was to use the prosrarn to project future energy use. The third was to use the prosrarn to estimate the effects of enersy conservation measures or development alternatives. The performance of these tasks by the test subject was evaluated.
Because it is of no use to computerize an audit technique which is not reliable* several cross-checks and comparisons were made to determine the reliability of the audit results. Eecause the City of Boulder had performed a very detailed energy audit* their fisures were used for comparison. When the Boulder input fisures for the residential and transportation sectors were used in the Office of EnerSy Conservation workbook analysis* the results were fairly close. Unfortunatley these were the only sectors with similar input units.
Insofar as it was possible* the consurnins sectors of Arvada and Boulder were compared. In the residential sector* the averase use per unit showed a tremendous variation. This resulted in the discovery of a significant error in the original input data. When the input was corrected* the results were relatively similar. The commercial sectors seemed reasonable* although detailed analysis was not made. The industrial sector showed the greatest discrepancy? with Arvada employees consuming almost four times the energy that Boulder employees

No error was found in data entry and no 1 os i ca 1
explantion could be made. Given that Boulder's analysis was made u s i n s actual utility bills and walk-throush audits of buildinSSr their results are felt to be more accurate. This makes the O.E.C. workbook analysis of industrial enersy use rather suspect.
In order to demonstrate that computerized calculation of the worksheets is preferable to hand calculation) the same input was used to compare the two processes. The results clearly demonstrate the superiority of the computer
method. Us i ns the computer 7 about ten mi nutes i s requi red
to enter all datar to receive all totals for use and value
and to obtain a p r i n ted copy for all sectors. The same
results require over eisht hours if performed by hand! and
hand-calculated totals are more likely to contain errors.
In chapter seven> the technique is evaluated in terms of the established criteria. Recommendations are made for improvement of this technique. These relate to both the audit itself and the method of computerization. Further research is sussested in both areas.
0.3 Limits of the Study
The field of community enersy plannins is quite youns. There has been little experience in the use of community enersy auditsr even less verification of results or comparison of techniques. The audit technique used in this analysis had never been employed in the field previously.

No existing micro-computer' assisted techniques were available as examples to improve upon. Thus the character of this analysis is exploratory, the product is prototypica 1 Refinement of the audit technique and computer application will be left to future researchers.
It should be understood that community energy planning, and in particular the audit phase, as it is now practiced is more art than science. Results will not be exact, regardless the level of detail in the analysis. In conducting the audit a decision must be made concerning the trade-offs between accuracy of input and time and effort required to collect it. A maoar objective of this analysis was the simplification of the energy planning process. Thus the input data was collected in the quickest and easiest manner possible in order to demonstrate the "entry level" for community energy planning. However, recommendations are made for more rigorous data collection methods and means of verifying the input are suggested.
No modification of the engineering aspects of the audits has been considered. The grouping of common units for analysis and the determination of average use per unit can be accomplished only by detailed technical engineering studies which are beyond the scope of this project. When the average use factors supplied produced questionable results, this was pointed out, but correction of average use factors is the work of engineers, not planners.
Although the technique is designed to be capable of projecting the growth of energy demand, this is based on

the arowth of the community itself
A variety of methods
exist for mak i na community arowth projections. from simple straight line trends in population to complex reaional industrial employment models. The optimum technique for arowth projection is an appropriate subject for further study. For the purposes of this analysis it is sufficient that the system is flexible enouah to handle different types of arowth projections.
Likewise. the technique should be capable of determinina the effects of enerdy conservation measures. This is demonstrated with several examples. The actual savinas per unit resultind from a aiven conservation measure and the number of units affected must be supported independently. A "menu" of conservation measures and their related savinas would be an excellent contribution to the field of community eneray plannina. Such a project is a mammoth undertakina thouahr and is beyond the scope of this study .
0.4 Conclusions
This computer assisted technique offers distinct advantaaes over, hand calculated methods. It is somewhat complicated to use however. The next lasical step in its refinement would be an inter-active proaram. Such a prodram would be simpler to use. would offer less opportunity for introduction of error, and would produce more useful and readable print-out.

Before any effort is made to increase the sophistication of the computerization; however; the audit technique should be re-examined. Three major improvements are suggested. The data collection for the commercial sector should be simplified. The municipal sector should be expanded. The industrial sector average use values should be verified.
Even in its current form; though; this computer assisted technique is thought to be superior to other community energy planning methods; especially in terms of the relationship between effort expended and results
ach i eved

1.0 The Importance of Community Energy Planning
Community energy planning was virtually unheard of before 1970. This chapter examines the factors which led to the development of this field and the benefits to communities which undertake energy planning.
1.1 World Energy Supply Background
The oil embargo of 1973 marked the end of an era for America! the era of cheaPi plentiful energy supplies^ when no thought was given to reducing energy consumption of our cars, homes or communities. For most Americans, it was waiting in long lines to buy gasoline that gave meaning to the concept of "energy crisis". Actually, the energy crisis of 1973 was a reflection of political and economic pressures developing since the 1950's (1). While
previously the o i 1 companies had paid a sma11 royalty on
oil taken out of foreign nations, the oil producing
coun tries began to demand a share of the profits and
eventually took over control of production levels (2). Iran and Saudi Arabia have imposed boycotts of sales to the U.S. independently, and the creation of the Organization of Petroleum Exporting Countries has given the oil producing nations even more political clout.
The United States' political negotiating position in

the middle east is seriously compromised by our dependence
on the supply of oil from those nations. Furthermore! volatile political conditions and relatively unstable governments in the middle east could brine supply interruptions that have nothins to do with U.S. actions and are completely out of this countries' control.
Nuclear energy. once touted as a clean and cheap substitute for fossil fuels. is no longer seen as the panacea for our energy woes. Concern is growing over environmental degredationi problems with disposal of radioactive wastes. the threat of serious accidents (especially following the incindent at Three Mile Island) and design problems with plants under construction. Beyond these objections! sheer economics are halting the proliferation of nuclear power plants. The plants are simply too expensive to build and maintain! and they cannot compete with coal fired plants (3).
1.2 The Impact of the Energy Crisis on the Nation
While the energy crisis has hampered this country's ability to negotiate in world political circles, its most devastating effect has been on our national economy. The increase in energy prices in the last ten years is one of the major causes of inflation in this country's economy. Certain industries. such as pharmacutica1s. plastics, and fertilizers. use petroleum as their primary raw material. All products have energy inputs in the manufacturing and

distribution processes. Of courser energy is required for the provision of services as well. Thus any increase in enerdy costs reverberates through the econorny, inflating the price of goods and services many times over.
More difficult to assess is the impact of uncertainty about energy supplies. The fear of shortages and uncertainty about future energy costs cloud businesses' decision-making process and discourage expanded production or new enterprise. While the energy crisis is not the sole source of this country's economic woes, it is certainly a major contributor.
1.3 The Impact of the Energy Crisis on Communities
The most obvious and immediate impact the energy crisis has had on communities is on the city budget itself. Most city budgets include a considerable expenditure for utilities. This usually includes heating, cooling and lighting municipal buildings, street lighting, operation of water treatment and wastewater facilities and their associated pumps and lift stations. Another major energy expenditure for local government is the gasoline to operate vehicle fleets,' police, fire and city vehicles. Rapidly escalating energy costs are a tremendous burden for city financial officers. When costs so up, cities have two choices! cut back on services provided or increase taxes. Neither alternative is very popular with residents.
More subtle, but far more devastating is the effect of

rising energy costs
on the entire local economy
communities produce the energy used locally. so money spent on utilities is drained off the local economy. Residents spending a greater proportion of their incomes on gas and utilities have less to spend in local shops. restaurants and other businesses. Decreased business activity results in fewer jobs, less investment, and of course, less sales tax revenue. This has a harmfull impact on those communities. like many in front range Colorado, which rely heavily on the sales tax for their revenue. The City of Boulder calculated that its residents spent $119 million on utilities and gas in 1930. If the city were to achieve a modest 10% energy conservation, an additional $12 million would return to the local economy (4).
Beyond the economic effects are the "duality of life" effects! how people's lives are changed as a result of high energy prices. Local citizens have less money available to spend on amenities. or. depending on their income level, necessities. The poor are hit the hardest! they spend a greater proportion of their incomes on utility energy costs (5). For the elderly and those on a fixed incomes, rising energy costs have a particularly severe impact.
Another unfortunate impact of energy price increases is that home ownership becomes unattainable for people of moderate incomes. Is is now a regular practice for lending institutions to consider monthly utility bills when assessing one's financial ability to keep up house payments. A home which cost $40 per month to heat a few

years ago. may now cost upwards of $100 per month. Added to hish rnortsase payments at today's interest rates. the utility cost may push the total monthly expenditures beyond the individual's finacial resources and the loan will be refused .
So far this discussion has focussed on the impacts of energy price increases. yet the impacts of an extended energy shortage must be considered. as our reliance on foreign supplies leaves this country and its communities vulnerable to this possiblity.
Initially those industries which rely heavily on imported oil would shut down. as would marginally profitable businesses. Increased unemployment would lead a downward spiral of the economy. Colorado might fare better than the nation as a whole, with its local resources of coal and natural gas. Yet it must be remembered that the fairly minor shortages of gasoline in 1973 and 1979 had a very detrimental effect on tourism. the state's second largest industry (6).
Perhaps the most insidious effect of the energy crisis is the loss of the community's ability to control its own destiny. Self-reliance and self-sufficiency, foundations of this nation's political philosophy, have been seriously eroded. In many communities, it is the desire to regain control of the future, as much as mitigation of economic impacts. which has prompted the development of community energy conservation plans.

1.4 Appropriateness of the Local Level for Energy Planning
Energy consumption is closely related to land use and because land use is traditionaly regulated on the local level. it is appropriate that energy planning be incorporated into land use planning and regulation. Many barriers to energy conservation exist at the local level and therefore must be eliminated by local action. Examples of these barriers are zoning regulations which prohibit mixed uses. or height restrictions which would preclude roof top solar collectors.
No national. or even state. energy policy could adequately address the varied conditions in all communities. The type and age of structures, development patterns. industrial mix. energy resources available, and climate will determine unique local strategies for energy conservation. A plan that works well for Davis. California would be a failure for Newark. New Jersey. To be effective, energy planning must be done on the local level.
Commun i ty energy conservat i on re 1i es heavily on
"outreach" p ro g rams such as seminars on home
weatherization . carpoolins programs, or demonstration solar
installations. When the outreach programs originate at the local level residents are more receptive than if the same program was provided by a federal agency.
A key factor from a phi1osophica 1 standpoint, is that local energy planning returns to the community control over its own destiny.

1.5 Benefits of Eneray Planning and Conservation Prosrams
An effective eneray plan and conservation program will reap many benefits. The primary effect is the mitigation of economic ills described earlier; the strain on city budgets and the drain of dollars from the local economy. Some cities have developed contingency plans to ease the threat of shortages.
Beyond the obvious and direct benefits, there are several positive side effects" of energy planning. Most energy conservation measures have a positive environmental impact. Less automobile travel means less automobile
pollution. Energy conserving land use patterns are also land conserving, preserving open space or agricultural land .
Increased reliance on renewable resources, such as solar heating, will boost employment opportunities (7).
Energy planning also offers an opportuniy for citizen participation which in turn leads to increased community cohesiveness. Furthermore, each community which reduces energy consumption contributes, albeit slightly, to this county's freedom from dependence on foreign energy
1.6 Problems with Energy Planning
It would not be realistic to end this introduction

without mentioning some of the problems with* and barriers tor community energy planning.
It is felt by some that energy consumption and conservation decisions should be left to the private market. If prices get too high, they reason* individual
consumers will initiate conservation measures. If a majority* or key members* of a local government feel energy planning is not a proper role for government* then it will not take place in that community* unless they are convinced otherwise. Two arguments answer this position. First* residents must be informed of the range of conservation possibilities in order to make the best decision. Second* there are certain barriers to conservation which must be removed by local government action* such as zoning restrictions. If for no other reasons* then* local government should have a hand in energy planning.
Some local officials favor energy conservation but feel detailed analysis of the community's present and future energy use is a waste of time. If conservation measures are known to be effective* they feel* then so ahead and implement them. This approach actually has a great deal of merit. Some communities have gotten hopelessly bogged down while trying to pinpoint every last BTU being used. The momentum of the planning effort is lost* and conservation measures are never adopted. While a thorough analysis of local energy use is the best basis for planning conservation strategies* it is certainly better to implement conservation measures without a plan* than to

take no action in favor of conservation in the absence of
a detailed energy use analysis.
At the outset of the energy planning projecti a decision has to be made about the trade-offs between the level of detail and accuracy sought in the data collection process and the amount of resources available for the entire project. Staff time and money may be better-utilized in the implementation Phaser rather than in the research phase. This decision process is made more difficult by the fact that most communities are inexperienced in this field and are dealing with unknown quantities.
Although energy planning is an appropriate project for citizen volunteers/ some funds and staff time will be required. Depending on the scale and sophistication of the analysis and plan. the demand on city resources may be considerable. If a city is operating under fiscal constraints/ it will require some marketing of the values of energy planning in order to justify spending the money on a long range plannning project of this sort. The strongest selling point seems to be the amount of money which will be kept in the local economy as a result of energy conservation.
Because it is a relatively new field/ few cities have staff experienced in energy planning. Although many handbooks and manuals available are meant to be self-explanatory/ the first attempt to use them may be very frustrating.

1 7 Surnma r y
In this chapter the field of community energy planning has been introduced. Background was given in the international causes of the energy crisis^ rapidly escalating energy prices^ and the political and economic impacts on the United States. The impacts on communities from energy price increases and the threat of energy shortages demonstrate the benefits of planning for energy conservation. Local government is the appropriate level for energy planning because of ties to land use planning and the need to respond to unique local conditions. Benefits of energy planning include a healthier local economy ability to withstand energy supply shortasesr and improved environmental conditions. Problems in the community energy planning field include resistence on the part of local officials and the lack of simpler clear and accurate means of analyzing a community's energy use.
Chapter one established the benefits of community energy planning and pointed out some of the problems in the field. Chapter two will develop the criteria for a useful
tool for community energy planning.

2.0 Criteria for a Useful Community Energy Planning Tool
If one accepts the premise that community energy planning is beneficial. the next task is to define the characteristics of a useful community energy planning tool. These characteristics. or criteria, are largely based on common sense.' the tool has to work at the community level. In addition I have imposed another condition? that the tool should not require resources. including expertise of personnel. equipment. or cost. beyond what is normally available in the local government for a project of this type. Very small towns without planning staff might have to rely on volunteer help, but it would come from within
the community rather than from outside experts". I have
imposed this requirement in keeping with the philosophy of community self-reliance. Since self-reliance is a goal of most communities which undertake energy planning, it is only fitting that the community be capable of completing an energy plan unassisted.
The criteria for a community energy planning tool are described in more detail in the sections which follow.
2.1 Local Applicability
To be useful to a community, any energy planning tool must be applicable at the local level. It must be capable of di5-aggresatinS fuel use at the community level. Thus a

macro-economic model which relys on forces operating at a
much larser scale could not be adapted for use within the boundaries of a single community.
In addition to the problem of scaler is the problem of unique local conditions which affect fuel use. For examplBr it is unlikely that Colorado State average consumption figures for home heating would reflect use in Leadville. which has co1der-than-average winters. A useful community energy planning tool must be capable of integrating unique local conditionsr including
climatological factors. end use factors if they vary from disaggregated averages, and local fuel supply factors.
In summary, the local applicability criterium requires that the community energy planning tool be capable of data collection or disaggregation at the local community level, and be sensitive to local conditions.
2.2 Comprehensive Analysis
A useful community energy planning tool must be comprehensive in two aspects! it must include analysis of all major fuel types as well as all consuming sectors. An analysis of a single fuel type may have its purposes but it will not be suitable for community energy planning. The only possible exception would be a series of single fuel analyses which were designed to be aggregated in such a manner as to allow consideration of inter-fuel substitution. Likewise. single-sector analysis. e.g.

residential consumption, would be useful only if compatible
analyses were available for all other sectors.
2.3 Within Local Means
It would be entirely possible for a central asency-federal. state. or sub-state council of sovernments to develop and maintain a sophisticated community energy planning tool. The agency would provide expertise and computer resources. The community would supply their input data and would receive the completed analysis from the agency. I have rejected this option for the purposes of my analysis for several reasons. First, changes in political administrations brinS changes in priorities. One
administration misht fully support development of a
community energy planning tool. another misht decide to charge communities for use of the service, and a third misht eliminate the program entirely. A basic motivation in undertakinS community energy planning is a desire for community self-reliance. It is only fittins that the
community energy planning tool be useable within the community unaided by outside "experts".
The useful community energy planning tool should allow for variation in local personnel resources available for the task. Some energy concious communities have staff whose sole function is energy Flanning. In the community without an energy planner, the task would loSically fall on the land use planner. Thus the community energy planning

tool should be designed for the technical level of the
average city planner. Alternatively* some communities have citizen volunteers* with varied technical backgrounds* undertaking the energy planning process. Thus the tool should not require sophisticated training in statistical analysis or fundamentals of energy use.
There is major benefit to having the energy planning process originate locally* particularly if citizen participation is a part of the process. When the process enters the implementation stage* residents are apt to be more cooperative if they feel its "their" plan* rather than something handed down from the state or federal government.
For these reasons* I have rejected the possibility of a central agency providing the community energy planning
tool* in favor of a technique which could be accomplished
within the community* using local resources.
2.4 Time and Effort
In early community energy planning efforts* the data collection phase alone required many people working for six months to a year. Most communities will not be interested in undertaking a project of that magnitude* particulary in the absence of grant money to do so. There are some highly simplified techniques available for community energy planning which mitigate the data collection problem. However* in some cases* the oversimplification casts doubt
on the quality of the results.

What then is the optimum amount of effort to spent in
the data collection phase of the community energy planning process? Each community must decide that for itself* based on its resources and the purpose to which the plan will be put. A good community energy planning tool might offer the option of using varying levels of detail* according to the community's needs.
In any case* the input data necessary should come from existing secondary sources* such as census publications* or be readily available without extensive time and effort required.
2.5 Computerization
Given the complexity of a community's energy use*
i.e.j various fuel types* sectors* subsectors and end uses within subsectors* the only reasonable way to handle the community energy planning process is with the aid of a computer program of some sort. Hand calculations of this amount of data are subject to error* as well as being extremely tedious.
Once the basic data for current energy use is
collected* most communities will want several sets of future projections for different numbers of years in the future* for different rates of growth in the community and for the effects of different energy conservation policies. This sort of extensive number crunching is more suited to machine than man. If it has to be done by hand* it may not

set clone
2.6 Assesses Alternatives
It is useful to a local officials to know how much energy is being consumed in their community? estimates of future demand are useful as well. But to be truly effective as a planning tooh a community energy planning technique must have some means of showing the effects of a variety of conditions or policy alternatives. It is this
capability which makes the tool effective in policy
formulation. Local officials need to know more than simply the number of Btu's that will be required by the communilty five years from now, or the value of dollars leaving the
economy as a result. Decision makers need to know the
implications of certain actions? if they pass a given ordinance* how many BTU's and dollars will be saved as a result? An energy planning technique which cannot at least estimate energy savings which result from conservation efforts is not an effective energy planning tool.
2.7 Surnma ry
In this chapter the criteria for a community energy planning tool have been established. The criteria point to a technique which is appropriate for use by a local government. The scope of the analysis should address the
geoaraphicaly and the local resources in terms
local area

of technical requirements and cost, exist? To answer that question? number of energy planning models and
Does such a chapter three tec hn i ques.
technique reviews a

3.0 Existing Tools for Community Energy Planning
Existing tools or techniques for energy modeling and planning were reviewed to determine if a technique which fulfills the previously stated criteria is currently available. Table One on the following page summarizes the findings. Erief descriptions of the models and techniques, and evalualtions of their suitability for community energy planning follow Table One.
2 3

ifl i i
o 1.1
3.1 Brook haven maybe
3.2 Community Energy Planning yes
3.3 County Energy Guidebook maybe
3.4 Hittman Methodology yes
3.5 Linear Proar a mm ina in a v b e
3.6 Macro Economic Models no
3.7 MATH/CHRDS maybe
3.8 OASIS no
3.9 ORNL mavbe
3.10 Planner's Energy Workbook yes
3.11 READ no
3.12 RDFOR no
3.13 SOLARSIM/Solar Market maybe
3.14 SPURR no

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Iti C *
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C s: £ ui
ill > r- in
ri H D <
rH d Hi
IJ1 u 0 M
C o rH o
Ol jU S-
m c c 4-> r~*
s_ r- o JU
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s m £ U.
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04 O'* u.* -.o
04 04 04 04 Of
yes n o n o yes ma y b e
yes yes yes ri o ma y be
yes yes yes n o ma y b e
yes maybe ri o n o lit a y b m
n o n o II u yes ri u
m a y b e ri o ma y b e yes ma v b e
n a n o yes yes yes
n o n o yes yes n o
n o n o yes yes n o
y e s yes ina y b e n o ma v b e
maybe n o rn a y b e yes n o
yes n o ma y be yes n o
n o n o n o yes yes
yes n o ma y be y e 5 yes

There are literally thousands of models dealing with energy use of one sort or another. Those which are obviously inappropriate have been eliminated. In some cases! single examples have been chosen to represent a SrouF of similar models.
3.1 Brookhaven Land Use and Energy Utilization Model
This land use energy simulation model was developed by Brookhaven National Laboratory and the State University of New York at Stony Brook as part of the Land Use and Energy Utilization Project. The research/ sponsored by the Federal Energy Administration! was undertaken to explore the relationship between alternate land use patterns and
energy and fuel demands. The purpose was to facilitate the determination of projected demand on regional energy supplies and distribution systems based on three
development pattern scenarios. The model employs the basic structure of the Lowry land use modelr with disaggregation of the housing! commercial and industrial sectors to facilitate study of energy demand factors.
Of all the energy use models examined/ the Brookhaven model appears to most nearly fulfill the requirements for community energy planning. The model predicts energy
impacts of land use decisions. It was not developed as a community energy planning tool/ however/ and there are
several reasons why it is not applicable for this purpose

Gathering the input data would be an enormous task* as the
region must be divided into a grid pattern and acreage of land uses measured in each. The model is applicable only to a free-standing community or an entire self-contained region. This precludes its use by a smaller entity within an urban area. However* the Brookhaven model was not intended to be used as a community energy planning tool* as T. Owen Carroll explains!
The land use-energy simulation model proves useful in assessing energy implications of a broad variety of alternative land use patterns which may be considered in the Flannins process. From the planner's perspective* the issue is not how to achieve energy savings per se (S).
3.2 "Community Energy Planning-The Basic Elements"
This workbook technique was developed in the Colorado State Office of Energy Conservation by Ann Jones* a former employee of Hittman Associates. It is a simplification of the Hittman technique which identifies certain community "units" and average energy consumption per unit. Six consuming sectors are analyzed. Default values* in the form of Colorado averages* are offered in many cases. Calculation is completely simplified and non-thrsatenina to the non-mathematician. Required input* for the most part* is readily available. It is the simplest* clearest and
most flexible of the workbook t-echniqes. Despite its
qua!i t i es
it has not* to Ms. Jones' knowlese

ever been used by a community
3.3 "County Energy Guidebook"
"County Energy Plan Guidebook} Creating a Renewable Energy Future" was written by Alan Okasaki and Jim Benson of the Institute for Ecological Policies. Four sectors are identified in the method; and total energy consumption for each sector is first calculated. Extensive default values are used to calculate end uses within each sector. Projected future use is based on population growth only. Techniques for analyzing potential of renewable resources are given.
The purpose of this technique is to provide a quickT and simple means of identifying the future potential for renewable resources. It is a simplification of the Hittrnan methodololgy adapted for use at the county level. It is diametrically opposed to the Hittrnan methodology in two aspects? the time required to perform the audit and the accuracy of the results. In his analysis of audit techniques (9); Pferdehirt reported on the experience of the communities involved in the Comprehensive Community Energy Planning Project (C.C.E.M.P. is a federally funded research project involving sixteen communities' energy audits and conservation plans.) as well as several other common audit techniques. Pferdehirt points out the major

Computationally easy to apply* but with many simplifying assumptions whose effects are inadequately explained...may not produce sufficiently accurate results for a particular application! but the reader is not alerted to the errors potentially introduced by the simplifies ton in the methodology.
The County Energy Pla'n Guidebook." was not considered suitable for this project because of its large margin for error* and because its application is limited to the county level .
3.4 Hittman Methodology
The Hittman methodology* as it is commonly referred to* consists of three volumes in a series called
"Comprehensive Community Energy Planning"
The first
volume is the workbook* the second consists of appendices and the third contains the work sheets themselves. Although the methodology should yield fairly precise results* the task of assembling the input data is nearly impossible. Over 36*000 separate values must be found to fill in the 6*000 spaces in the work book. The sixteen communities in the Comprehensive Community Energy Planning Study all used* or tried to use* the Hittman methodology. In his analysis of audit techniques* Pferdehirt reported on the experience of the C.C.E.M.P. communities with this method (10)1
The greatest defect in the Hittman audit is its enormous detail* much of which seems like a trivia

exercise with the object of tracing every sinsle Etu in the community. Some Planners who have used the audit questioned whether the detail is worth the time and expense required to Sene rate it.
Pferdehirt also feels the method is poorly referenced; in terms of default values? does not address renewable resources? and is limited to use in cities with a population Greater than 25r000. One of the cities in the C.C.E.M.P. pros ram is Boulder. In its case the audit usinS the Hittman methodoloSy required two consultins firms and the Ensineerins Department of the University of Colorado; and took nearly a year to complete. This demand on staff and resouces places the Hittrnan methodoloSy outside the reasonable limits for local enerSy planning exercises.
3.5 "Linear Prosrammins Approach to County Level EnerSy
Scenario DisaSSresation"
This is a linear prosrammins disaSsresation procedure to translate resional enersy scenarios to county level detail; with emphasis on electric and coal sectors; allowins direct assessments of facility sitinS; resource constraint and environmental impact considerations as an inherent component of scenario analysis.
This could be a useful tool; assumins there is a recent resional enersy scenario available to disassrasate. It is not apparent whether or not the procedure is applicable at the community level. A constraint for a community wishins to use this procedure is that only the

model description! and not the It would entail considerable running. Effects of alternative not considered in this mode developed separately.
program coder is available.
expense to Set this up and conservation techniques are these would have to be
3.6 Macro-economic Models
There are a number of macro-economic models for analysis of energy supply and demand at the national level. The Hudson-Jorgenson and Hynilicza are in fact models of the entire U.S. economy. with emphasis on the energy sector. The PIES and SRI models aggregate regional prices and quantities. TERA. developed by the American Gas
Association. focusses on the gas industry, but includes a
substantial amount of information on other types of energy. TERA covers supply and demand price equilibrium among fuel types as well as technology changes and regulation. ETA-MACRO. developed at Stanford University is a hybrid of
two existing models, one which analyzed penetration of new technology and a macro-economic model. The resulting model allows analysis of new energy technology within the context of the national economy. ETA-MACRO is capable of assessing inter-fuel substitution. cost-effective conservation and new supply technologies in the energy/economy interaction.
These macro-ecomomic models have been used to evaluate alternative energy technologies. to study the economic
inteac ts
of alternative energy futures.
to study nuclear

to study the effects of oil and
natural Sa s price decontrol! and to provide background data used in the development national energy legislation.
In his analysis of several of these models. Brooks succinctly pointed out a serious limitation!
A common limitation of all five models is that they are economic in naturer i.e.. their aim is to project prices and quantities of energy sources. A serious question arises as to the ultimate usefulness of price-quantity data in the analysis of policy problems such as national energy policy which are pregnant with moral and political considerations (11).
While ETA-MACRO offers a broader analysis. it shares another limitation of all of these models in terms of potential use for communities! macro-economic analysis
cannot be applied at the community level. While these
macro-economic models might provide some valuable input> specificly price and supply information, they are by their very nature unsuitable for community energy planning.
3.7 "MATH/CHRDS" and "Indirect Effects of Micros1 mu 1 ation
MATH/CHRDS analyzes the impacts of changing prices and broader energy policy changes on household direct-energy expenditures by various population sub-groups. It also describes the ways in which important energy-related household characteristics change over time in response to economic, demographic and energy changes.

The model acts on household level data. provided by the
census public use sample. It simulates a sample survey of households^ the projects the households to a future year by updating demo3raphic economic. and energy related characteristics. By simulating the survey. it allows analysis of different population subgroups such as the elderly or poor.
Of all the models examined. MATH/CHRDS has the most uniaue approach. and one which offers the sreatest potential for application to community energy planning. If the public use sample, now available at the county level, could be made available at the community level, there is no apparenty reason why the model couldn't be applied to community energy planning. It is currently applied at the
national and Department of Energy Regions level.
MATH/CHRDS was developed using 1970 census data. In the 1930 census, detailed information was collected on a sample basis concerning household energy consumption, equipment and conservation measures such as storm windows and insulation. If MATH/CHRDS were updated to include this information it would most likely be the most powerful tool available for modeling energy consumption and the effects of policy changes.
This model is far superior to most econometric models. It allows for analysis of the effects of alternative policies. not just on the entire population but by
subgroups. Because it utilizes census data, the input is
very locally
specific and requires little local data

The disadvantages of MATH/CHRD5 is that the output is strictly economic. For energy management PUFases*
communities require i n f o rrna t i on on fuel use by type. Although its reliance on census data has certain advantages* it has disadvantages tool presently the census is collected only decennially* and is not available until two to three years after collection. The model is also at the top end of the scale in terms of the size of the program and equipment needed to run it. It would have to be maintained by a fairly sophisticated data processing center at the state level* or prehaps by a university.
The Indirect Effects of Microsimulation System was designed to be use in conjunction with MATH/CHRDS. It analyzes the indirect impacts of alternate energy futures on households and individuals. These impacts would be the effect on employment and consumption of consumer goods. Although it doesn't have direct connection with community energy planning* this analysis would be of interest to
communities* particularly the employment aspect.
3.3 "OASIS"
OASIS was developed by Arsonne National Laboratory to aid in analyzing and designing community energy systems* such as district heating or co-generation systems. It simulates plant operation based on user-supplied information about demand and equipment.

OASIS is applicable at the sub-community level in the
design of
used to
for the
cornmu n i t y
integrated district utility systems. It could be analyze the dollars and energy saved by using heating systems as opposed to conventional One enormous advantage of OASIS is that the code program is available directly from Argonns via hookup to their computer code resource center, the program is not applicable to comprehensive energy management.
3.? ORNL "Engineering-Economic Model of Residential Use"
The ORNL residential use model was developed to
simulate energy use in the residential sector from 1970 to
2000. It deals with four fuel types, eight end uses and
three housing types. Structure thermal performance, equipment costs, fuel costs and costs to improve the structure's thermal performance are considered. The model was employed to simulate residential energy use from 1960 to 1976 and it was shown to accurately predict aggregate energy use, energy use by fuel type and equipment ownership market shares.
It is not clear from the description whether the model can be applied at the community level. One would think not, since it only evaluates three housing types. In any case, it would not be suitable for community energy planning, since it is not comprehensive.

3.10 "Planner's Energy Workbook
Planner's Energy Workbook was developed in conjunction with the Erookhaven/S.U.N.Y. Land Use and Energy Utilization study. This workbook predates the Hittrnan methodology* but the approaches are similar. Planner's Energy Workbook is simpler than Hittman's in terms of the number of categories of sectorsi subsectors and end uses and requires far less input data. Computationaly it is the most sophisticated of the workbook techniques reviewed.
Although Planner's Energy Workbook was considered as a potential candidate for computerization in this projects it was eliminated for two reasons. The default values provided are national rather than local > and are fairly
dated. More sisnificant1y* the default values* particulary
in the industrial category* appear to be very high* as much as ten times greater than equivalent data from other sources* according to Pferdehirt (12).
3.11 "Regional Energy Activity and Demographic Model" (READ)
The purpose of this model is to provide energy sensitive regional economic and demographic projections to
be used a s inputs to the Mi d-term Energy Forecasting
Sy st ern* and the analyze the impacts that are forecast by
MEFS I t i s essentially an industrial location model using
the county as the basic geographic unit for input. It has

flexible aggregation capability* potential for- supporting
environmental considerations* and interfaces with MATH/CHRDS. Some of the input data may be fairly difficult for the average planner to come up with* particularly the input-/ output capital flows coeffients. The output of the program includes the industrial outFut* industrial employment* earnings* investment* and capital stocks** income* population* and state and local finances* residential investment and housing stock; and demand for industrial output.
READ output provides valuable information* not just
for energy planning* but for general community planning.
It is not an energy planning tool* however. Its lowest
geographic level of operation is probably regional* and in
any case it would not serve for community energy planning
as it does not allow for policy alternatives.
3.12 "Regional Energy Demand Forecasting Model'1 (RDFOR)
Primarily econometric* RDFQR forecasts fuel demanded by region as a function of prices* other macro-economic variables and population. It describes the interaction
between economic activity levels and energy prices for fuels by sector and demand for enegy in those sectors by fuel t-ype.
RDFOR operates at the resional rather than national level* but it has the inherent limitations of a

macr-o-economic models as far- as its applicability to
community energy planning,
3.13 "Simulation of Solar System Performance and Market Penetration Model" (SOLARSIM) and "Solar Market Development Model"
SOLARSIM designs OFtimum solar water and space heating systems for residential and commercial buildings. then calculates the market Fenetration under various incentive programs. Input required includes weather data. building loads. initial investment and operating costsi mortgage
duration and interest rate. incomer property tax rate and consumer discount rate. The output is percentage of
traditional fuel replaced by solar and payback period.
The Solar Market Development Model evaluates the
impacts of changes in solar costs and benefits under
various kinds of incentive programs. It looks at ten building/market types, retrofit and new construction, three options for use (water heating only. space and water heating. and space heating and cooling and water heating), and seven alternative fuels. The program looks at the factors on an annual basis. and both financial and
non-financia 1 attractiveness is considered.
Either of these models. but particularly the Solar Market Development Model. might be a useful tool for analyzing the best means for encouraging the use of renewable solar resources. These programs are unsuitable

for community energy plannina general lyf because they are
not comprehensive.
3.14 "System for Projecting the Utilization of Renewable Resources" (SPURR)
SPURR examines the likely impact of future fuel castsi incentive prodramsr energy demands* solar and competing technology costs* and market acceptance of solar options. It involves simulation on a data base containing
engineering costs and other data on heating and cooling of residential buildings* agricultural and industrial process heat* centralized electricity generation, and synthetic fuels and products. Alternative technologies considered
are wind* thermal and photovoltaic solar* ocean thermal and
biomass. Analysis is at regional and national level* by fuel replaced.
Because SPURR utilizes macro-economic considerations* it is not applicable to the local level. While not
suitable for community energy planning, SPURR's regional output would be of interest to a community involved in energy planning.
4 2

3.15 Surnina r y
The energy models and planning techniques reviewed in this chapter fall into tour general categories. The first category is the macro-econom1c series, including J.o. d.V and 3.12. The strengths or these models are that the v are fairly accurate, many have been verified with actual data. The weakness of these models, in terms of community energy planning, is that they cannot be used on a small scale, i e. at the local level. The second category of models is the large scale energy-use models, including 3.1. 3.5.
3.7. 3.11. and 3.12. The models focus specificly on energy
use rather than the price/demand interaction, but snare the same limitation of the mac r o-ec on omic models! they cannot generally be used in a single community. The third group' of models are the predictive or optimizing special purpose models, including 3.3, 3.13, and 3.14. These have
interesting but limited application. Although they could be used in conjuction with a community energy plan, they cannot form the basis for such a plan. In the final category are the hand-calculated workbook techniques, including 3.2, 3.3, and 3.4. The strength of these method# is that they were developed specificly tor community (or county) energy planning. The analysis ot energy is scaled
to the local level, as is the required input. The problem with these techniques is that they require very tedious and potentially error-ridden hand calculation. This hand

calculation makes it difficult to update
or incorporate
changes or to test the effects of growth or conservation techniques.
In chapter three it is demonstrated that no technique exists which meets all the criteria developed in chapter two. Chapter four will outline a method for creating a community planning technique which does meet all the stated
4 3

4.0 Development of a Micro-computer Assissted Technique for
Community Energy Plannina
The review of existina eneray models and plannina techniques demonstrates that there is no technique which meets all criteria stated in chapter two. That is. one which is comprehensive across all fuels and consumins sectors, applicable at the community level, able to project future use and estimate the effects of policy alternatives, and computerized to aid in handlina of data.
Several approaches miaht yield a technique which would fulfill all of the stated criteria. One of the larae scale comprehensive models miaht be scaled down and adapted to the local level of analysis. Several sinale sector or sinale fuel models miaht be combined. One of the non-computerized workbook techniques could be adapted for use with a computer. The latter was chosen as the simplest means of achievina the end. The workbooks' data collection methods and eneray use analysis were developed for use at the local level. There is less chance of introducina error and untested relationships in the computerization of a technique developed for analysis of eneray use at the local level than in the sealina-down of a method developed for a larger scale of analysis. In simply computerizina the workbook calculation, no relationships will be changed.
4.1 Selection of Workbook for Computerization

Several of the better known workbook techniques were
reviewed and assessed for their suitability for this project. In addition to those discussed in chapter three? the Sizemore and Real Estate Research workbook techniques were considered. The Colorado Office of Energy
Conservation workbook? Community Enersy Planning? The Easic Elements" was selected as the OFtimum choice for this project. It is a relatively recent publication which addressed and rectified many of the problems of the earlier workbook techniques. "Community Enersy Plannins" offers two distinct advantages in terms of this project. It provides clear and simple means of data assembly and calculation and when default values or average uses are provided? the figures are Colorado State averages? rather than national averages. It is assumed this will provide more reliable results when used in Colorado.
4.2 Selection of Hardware and Software
The general criteria presented in Chapter two dictated certain constraints on the method of computerization. The requirement that the necessary equipment be readily available to local governments pointed to use of the now ubiquitous micro-computer (such as the Apple? Atari? or TRS-30) .
The requirement that the technique not require sophisticated programming skills suggested use of existing

software or development of a fairly fool-Froof and seneraly
adaptable inter-active Frog ram. The latter approach was discarded because it would entail adaptation to each of the various machines on which it would operate> calling for expertise not commonly found in local sovenments. Fortunately* there is existing software which is eminently suitable for this project. This software is marketed under-names such as "VisiCalc" or "SuFerCalc"* and is available for use on virtually every micro-computer. In fact* "VisiCalc" was the best selling single program for micro-caroputers in 1931. (13)
VisiCalc functions as a huge Piece of graph paper* containing about 250 rows and 60 columns. In each of the several thousand entry positions a label or a value may be entered. Formulas are written using entry positions as variables. Thus a value which is used repeatedly* e.S. Frice per kilowatt hour* need be changed in only one location and it will instantly be changed wherever it occurs on the sheet. The entire sheet rnay be saved on diskette and different versions of the same sheet, may be saved by giving each a different name.
Some of the capabilities of this program include the ability to replicate values or formulas repeatedly and relatively) to add columns or rows of numbers by referring to first and last entry in the list) to identify minimum and maximum values in a list? and to print out the figures displayed on the screen. The display may be changed to show fewer* wider columns or more* narrower columns. The

screen may be split to display two separate Farts of the
arid* or any row or column may be fixed while the rest of the screen continues to scroll.
The VisiCalc type of software was selected for this project for several reasons. It is readily available for nearly every available micro-computer. It is relatively inexpensive? the price is about two hundred dollars. It is easy to user and easy to learn, even for individuals with no experience on computers.
4.3 Adapting the "Community Energy Planning" Workbook to VisiCalc
The Office of Energy Conservation workbook. "Community
Energy Planning". contains thirty-one pages of worksheets
to be filled in. The size of the arid and simplification allowed by using VisiCalc enabled these thirty one pages to be reduced to six separate arids. In the computerized version there is a sinsle arid, or file" for each sector. In adaptina the worksheets to VisiCalc. care was taken to ensure that no chanaes were made in the numerical re 1 atianshiPS. The format changes made are documented in
the foliowina sections.
4.3.1 3 inale Family Residential
Throuahout the "Community Eneray Plannina" worksheets, the emphasis appears to be on clarity and simplicity of

calculations. Thus* if three values are to be multiplied ( e s number of dwelling units times percent using natural aas for heat times average annual fuel demand for a das furnace)- only two values will be multiplied at a time (e.S.- number of units times percent using natural aas for heat). This subtotal is then multiplied by the third value (average annual demand). It is assumed that the subtotal and the extra step are included to make calculations simple and non-threaten ins to users. In the computerized version-these extra steps have been eliminated because the computer is not threatened by complex calculations and because it allows the display of more sianificant data on the screen at one time.
Compare the sinsle family residential printout from the computerized version (figure 1.) to the sinsle family residential worksheets (fisures 2. and 3.). Column E from the worksheet- "unit of measure" has been eliminated because it is consistent throushout the entire worksheet on residential analysis. When the cursor is placed on the column title "units"- the display line elaborates- "sinsle family dwelling units". Entry position E3 is the only entry which must be changed to change the entire column B -because entries E4 through B 2 5 have been entered so as to replicate E3.
Percent" in position Cl corresponds to worksheet column E2. These values are available from Fublic utilities or state energy offices? they are often referred to as "appliance market shares" or "appliance penetration"
4 7

These values are generally available because
utilities need them to plan for residential energy demand. If recent figures are not available from the local public utility or the state? they are contained in the census reports entitled "Detailed Housing Characteristics'1 .
Column C from the workbook has been eliminated from the computerized version because it is a subtotal. Column D in the worksheet corresponds to Column D in the computerized version.
The worksheet column E? "Estimated Uses" corresponds to computer version Column E. When the cursor is placed on an entry E3 through E25? it will show the formula Ex = Ex times Cx times Dx. Eelow Column E calculations? energy units have been totaled by fuel type. These totals of fuel use by type are absent from the worksheet? but are of
interest to communities for the purpose of evaluating the effects of shortages of a particular fuel.
At this point in the computerized version? two columns related to price have been inserted. Price per unit of energy is entered in column F? and value of current energy use by subsector in column G. Total value of energy by the single family residential sector appears in position G 27. Eecause communities will want to consider the economic effects of energy demand at different price levels? and because prices for fuels appear in many locations? special entry positions have been created for fuel costs. These are F29 for electricity in Kilowatt hours? F31 for natural
gas in hundreds of cubic feet? F33 for Liquid Propane Gas

in a a 1 1 a n s ? and F3 5 for fuel oil in a a 1 1 a n s When prices for fuels are entered in these locations? they will automatically be changed throudhout the s i n a 1 e family residential calculations.
Column H in the computer version corresponds to column F in the worksheet. These values entered are averaaes for the Denver Metropolitan area, and should be checked when psrforrnins the sneray audit elsewhere. Total Stu appears in column G in the worksheet? column I in the computerized version. Entry position 127 shows total Etu consumed in the sinale family residential sector1 this total is missina f rora the worksheet version.
In order to project future enerav demand from the sindle family residential sector? users of the worksheets must fill in another entire pade of fiaures? complete with
calculations (f i a u r e 3.). Users of the computerized version need only chan ae one entry! S3. Then? to find the value of future demand? they may chanae the price per unit of e n e r a y ? in positions F 2 9 ? F31? F 3 3? and F35 (value of future enerSy demand is not included in the worksheets).
4 9


B1 X
Fuel Use By Type Unit of Measure Number of Units % of Units Using Fuel Type Number of Units Using Fuel Type Average Uses Estimated Uses Btu/Uni t of Fuel Type Total BtiP
Space Heating Dwel1ing Uiit
Natural Gas(CCF) .94 1091 85.000
Electricity(Kwh) II II .02^ 12.679 3.41 >
L.P.G. (GAL) II II .01 1120.67 91.600
Other Fuel Oil(GAL) H II .02^ 623.88 150.300
Water Heating Natural Gas(CCF) II II * 00 00 % 308 85.000
Elecricity (Kwh) II II .09p 3500 3.413
L.P.G. (GAL) II II .OlP 387.6 91.600
Other II II
Cooking Natural Gas(CCF) II II .28 40 85,000
Electricity (Kwh) II II .71 750 3,413
L.P.G. (GAL) II II % o o 40. R 91r600
Other Lighting (Kwh) II II l.ocP ... 855 3.413
Refrigerator (Kwh) II II l.ocP 1800 3.413
Washer (Kwh) II II .74P 103 3.413
Dryer (Kwh) II II .64P 993 3.413
Color TV (Kwh) II II .80P 320 3All
NOTE: Foot notes on following page.

1 7609
1 7609 . 854 1091 16406552
17609 . 053 12679 11833019
17609 .006 1120.67 118403.3
1 7609 . 003 623.83 32957.71
1 7609
1 7 609 .854 308 4 6 317 3 0
17609 .056 3500 3451364
17609 . 003 387.6 20475.75
17609 .003 0
1 7609
17609 . 28 40 197220.8
1 7609 .71 750 9376783 .
17609 .006 40.8 4310.683
17609 0
1 7609
1 7609 1 855 15055695
1 7609 1 1 800 31696200
1 7609 . 748 103 1356668.
1 7609 .646 993 11295786
17609 .307 320 4547348 .
TOTAL ELE 88612873
TOTAL NAT 21235503
TOTAL LPG 143189.7
TOTAL OTH 32957.71
3 48 1 5711121. 85000 1.35E 1 2
0574 679215.3 34 13 4.0 3 9 E 1 0
. 69 81698.26 91600 1 085E10
1.12 36912.63 150300 4.9 5 3 5 E 9
3481 1612305. 35 0 0 0 3.937E1 1
0574 198108.3 3413 1.178E10
. 69 14128.26 9 1 600 1.3756E9
3481 68652.56 85000 1.676E10
0574 538227.9 34 13 3.200E10
.69 2974.371 91600 3.9486E8
0574 864196.9 34 1 3 5.139E10
0574 1819362 . 3413 1.082E1 1
0574 77872.73 34 13 4.6303E9
0574 648378.1 3413 3.855E10
0574 261017.8 3413 1.552E10
12614171 TOTAL VAL

4.3.2 Multi-family Residential
Column A of both versions is the same, fuel use by type. Unit of measure, column B in the workbookr-has been eliminated from the computerized version, as the same unit of measure is used throughout. Number of units, El in the workbook is E on the computer. The percent using fuel type is C on the computer, E2 in the workbook. The subtotal in column C of the workbook has been eliminated in the computer version. Average use appears as column D and estimated use appears as column E in both versions. Fuel price per unit of energy is inserted in the computer version in column F? and value of fuel used in column G. This information is missins from the workbook version. Btu per unit of energy is Column F and total Btu per end use is column G in the workbook. These appear in columns H and I, respectively on the computer. The subsector total aFPears in entry position 17 of the computer vesionl this figure is absent from the worksheet. Total use in Kwh appears in entry position E7* and total value appears in entry position G9 on the computer. These figures are missing from the workbook version.
5 0

V' K K r I I W
Fuel Used By Type Unit of Measure Number of Units 7. of Units Using Fuel Type1 1 Number of Units Using Fuel Type Average Use Estimated Use Btu/Unit of Fuel Type Total Ittf'
Space Heatinp Natural Gas (CCF) Dwelling Unit .985 722 85,#00
Electricity (Kwh) H II .015 7309 3,413
All Other Needs Electricity (Kwh) II II 1.0 2831 3,413

Foot Notes: 0 Public Service Company of Colorado unpublished data for 1979 in the Denver Metro Area.
May be expressed in millions of Btu for ease of computation.

NAT GAS 3620 . 985 722 2574435. . 3431 396161.0 85000 2 138E11
ELEC 36 2 0 .0 15 7309 396878.7 .0574 22780.34 34 13 1 3 5 4 5 E 9
ALL OTHER 3 6 2 0 1 2831 10243220 .0574 538247.8 3413 3.493E10
TOTAL USE 13219534 TOTAL ETU 2.5 5 2 E 1 1
TOTAL VAL 1507190.

4.3.3 Commercial
The workbook contains seven worksheets for commercial use! base year and future electricity user base year and future sas user and base and future summaries. A third summary (worksheet 15) appears to be a duplication of worksheet 14.
All of the commercial worksheets have been incorporated into a sinsle file on the computer. Compare the computer printout to the worksheets. Followins the unit of measurer included because there are different units in the commercial sectorr is averase use of sas and estimated use of Sas. The computer automatically adds the estimated use column and provides a total Sas use in position E16. The price of sas is entered oncer in position E16. This value is then multiplied times the Sas use to obtain sas value in column Fr and total value of Sas used in the commercial sector in position F17. Gas Etu's are shown in column Gr with a total provided in position G1 6 Averase use of electricity appears in column Hr and estimated use in column I in the computer version. Total electricity demand by the commercial sector appears in position 117. Price per Kwh is entered once in position E17r then it is multiplied by each type of business' electricity use in column Jr with the total shown in position J16. Etu's of electricity appear in column Kr
with a total in position K16. Total Etu's
per commercial

use are shown in Column L and totaled in position M1 6 .
To determine future demand from the commercial sector, one simply changes the number of units in column C, and the estimated future price of aas and electricity for the target year in position B16 and E17.
The computerized version of commercial sector use offers several features absent from the worksheet version. The significant differences are the inclusion of value of energy consumed, and automatic totalling by business type
and by fuel type

Type of Business Unit of Measure No. of Units Average Use per Unit (ccf ) Estimated Use (ccf) Btu/ccf Total Btus (millions)
Food Stores' Sg. feet 1.02 85,000
Eating and Drinking Places Sq. feet 3.6 85,000
Enclosed Shopping Malls Sq. feet 1.06 85,000
Retail and Services (not in malls). Sq. feet 1.7 - 85,000
Hotels and Motels, etc. rooms 582.A 85,000
Warehouses, etc. Sq. feet 0.074 85,000
Ski facilities skier 0.48 85,000
Recreation other than skiing employee 1005.9 1 CD cn O o
Office Structures Sq, feet 3.18 85,000
Hospitals beds 2805.9 85,000 ]
Schools student 129.18 85,000
Universities and Colleges student 147.18 85,000
Construction million $ 0.007 85,000
Browne, Brotz, and-Coddinoton, 1980, using 1977 data.
* May be expressed in millions of Btu's for ease of computation.

B + C D

FOOD STR SO FT 267708 1.02 273062.2 2.321E10 80 21416640 7.309E10 9.6 31 E1 0
EAT/DRINKSQ FT 343204 3.6 1235534 . 1 050E1 1 56 19219424 6.560E10 1.706E11
ENC MALLSSQ FT 0 1.06 0 0 1 1 0 0 0
RETAIL 8 SQ FT 799112 1 7 1353490. 1 155E1 1 1 7 13584904 4.637E1 0 1.613E11
HOTELS 8 ROOMS 33 582.4 19219.2 1.6336E9 4398.7 145157.1 4.9 5 4 2 E 8 2. 1291E9
WAREHOUSESO FT 864376 .074 63963.82 5.4 3 6 9 E 9 9.5 8211572 2.3 0 3 E 1 0 3.346E10
SKI FACILSKIER 0 . 48 0 0 1 0 0 0 0
RECREATIOEMPLOYEE 34 1005.9 34200.6 2.9071E9 6998 237932 8.1206E3 3.7191E9
OFFICE SO FT 453193 3.18 1441154. 1.225E11 26 11783018 4.022E10 1 .627E1 1
HOSPITALSBEDS 0 2805.9 0 0 19996.6 0 0 0
SCHOOLS STUDENT 20758 129.18 2681518. 2.279E11 659.8 13696128 4.674E10 2.747E11
UNIV/COLLSTUDENT 0 147.18 0 0 1019.7 0 0 0
CONSTRUCTMILLION $ 22.5 . 007 . 1575 13337.5 . 058 1 305 4453.965 17341 .47
71 07 1 43 6.04 1E1 1
VALUE GAS 2487500.
.055E1 1
VALUE ELE 5739160

4.3.4 Industrial
The fifteen pages of worksheets on industrial energy use have been incorporated into a sinsle file in the computerized version. The SIC code is column A* the type of industry described by the SIC code is column E. Column B in the workbook* unit of measure has been eliminated* it is consistent throughout the industrial analysis.
What follows in the computerized version are the remaining columns of each of the worksheets* with certain modifications. Average use per unit and estimated use per unit is displayed as in the worksheets. Then value of estimated use is inserted and totaled at the bottom of the column. A separate column for Btu's per unit of energy has been eliminated* as it is consistent. Btu's per unit is simply inserted into the formula in the column following value for each energy type* to give total Btu's per industry. This same arrangement follows for each fuel type. Fuel use is totaled by type at the bottom of each "estimated use" column. Total value of all energy used by each industry appears in column AB and total Btu's consumed per industry appears in column AC. Totals for the entire industrial sector appear at the bottom of these columns* total value in position AE22 and total Btu's in AC 24.
The price per unit of energy is entered once* in columns B25 through B30. It should be noted that the units
the industrial sector analysis are an order of

magnitude higher than those used in the other sectors.
In order to project future demands one changes the number of employees in column C 7 and the projected price per unit of energy in B25 through B30. All the necessary calculation will then be performed by the computer.

A-l_________ A B Cx D = E x F = G
SIC Code Type of Industry Unit of Measure No. of Units Avg. Use per Unit ( MCF ) Estimated Use ( MCF ) Btu/ MCF Total Btus
20 Food and kindred products Employee 370.7 850,000
22 Textile mill products ll 117.0 _ 850,000
23 Apparel and other textile products ll 15.6 850,000
24 Lumber and wood products ll 103.3 850,000
25 Furniture and fixtures ll 23.2 850.000
26 Paper and allied products ll 471.8 850,000
27 Printing and publishing ll 19.3 850.000
28 Chemical's and allied products ll 260.6 850.000
29 Petroleum refining and related industries ll 2773.9 850.000
30 Rubber and miscellaneous plastic products ll 399.9 850.000
31 Leather and leather products ll 98.7 850,000
~3T~ Stone, clay, gTass, and concrete products ll 598.6 850.000
33 Primary metal industries ll 322.3 850,000
34 Fabricated metal products n 98.3 850,000
35 Machinery, except electrical ll 101 .9 850.000
36 Electrical machinery and equi pment ll 61.1 850,000
37 Transportation equipment ll 43.9 850,000
38 Measuring, analyzing, and controlling instruments ll 7.3 850,000
39 Miscellaneous manufacturing ll 44.5 850,000
Source: Colorado Energy Research Institute Report, Colorado Energy Consumption Patterns, prepared by
Browne, Brotz, and Coddington, 1980, using 1977 data for Denver Metro area.
* May be expressed in millions of Btu for ease of computation.


f -*

A-l_____________________A _______ B C x D = E x r = G
SIC Code Type of Industry Unit of Measure No. of Units Avg. Use per Unit ( Tons ) Estimated Use ( Tons ) Btu/ Ton Total Btus *
20 Food and kindred products Employee 6.4 22.400.000
22 Textile mill products
23 Apparel and other textile products
24 Lumber and wood products Employee 0.1 22.400.000
25 Furniture and fixtures
26 Paper and allied products Employee 6.2 22.400.000
27 Printing and publishing
28 Chemicals and allied products Employee 1.8 22.400.000
29 Petroleum refining and related industries
30 Rubber and miscellaneous plastic products Employee 5.5 22.400.000
31 Leather and leather products n 1.0 22,400,000
32~ Stone, clay, glass, ancf~ concrete products li 56.5 22.400.000
33 Primary metal industries II 126.8 22.400,000 ..
34 Fabricated metal products li 0.2 22,400,000 _
35 Machinery, except electrical li 0.3 22.400.000.
36 Electrical machinery and equipment li 0.3 22.400.000
37 Transportation equipment li 1.0 22,400,000
38 Measuring, analyzing, and controlling instruments
JS9 Miscellaneous manufacturing Employee 0.1 22,400,000
Source: Colorado Energy Research Institute Report, Colorado Energy Consumption Patterns, prepared by
Browne, Brotz, and Coddington, 1980, using 1977 data.
* May be expressed in Millions of Btu for ease of computation.

A-l_______________ A________________________B C x D = E x F = G
SIC Code Type of Industry Unit of Measure No. of Units Avg. Use per Unit (bbls ) Estimated Use ( bbls ) Btu/ bbls Total Btus **
20 Food and kindred products Employee 9.8 5.993.400'
22 Textile mill products ii 8.6 , 6.998.400
23 Apparel and other textile products II 0.8 5 993,400
24 Lumber and wood products II 7.3 5 [993'400
25 Furniture and fixtures II 1 .3 5..993.400
26 Paper and allied products II 62.3 5,993,400
27 Printing and publishing II 0.5 5,993,400
28 Chemicals and allied products II 24.9 5,993,400
29 Petroleum refining and related industries II 14.6 5.993.400
30 Rubber and miscellaneous plastic products II 13.8 5.993.400
31 Leather and leather products l( 9.0 5.993,400
32~ Stone,"clay, glass, and concrete products II 6.7 5.993,400
33 Primary metal industries II 2.1 5.993,400
34 Fabricated metal products II 1 7 ;_5^993^0fl
35 Machinery, except electrical ti 2.0 5,993..400 .
36 Electrical machinery and equipment 1 2 6,993,400
37 Transportation equipment II 1.4 6'993,400
38 . Measuring, analyzing, and controlling instruments II 2.3 5-.993^400 ...
39 Miscellaneous manufacturing u 1.6 5,993,400
Source: Colorado Energy Research Institute Report, Colorado Energy Consumption Patterns, prepared by Browne, Brotz, and Coddington, 1980, using 1977 data.
* This is the Average for the State of Colorado.
** May be expressed in millions of Btu for ease of computation.

A-l A B C x D E x F G
SIC Code Type of Industry Unit of Measure No. of Units Avg. Use per Unit ( bbls ) Estimated Use (bbls ) Btu/ bbl s Total Btus
20 Food and kindred products Employee 9.3 5,800,000
22 Textile mill products II 6.5 _ 5,800,000
23 Apparel and other textile products II 0.2 5,800,000
24 Lumber and wood products II 1.7 5,800,000
25 Furniture and fixtures II 0.4 .5,800,000
26 Paper and allied products II 76.0 5,800,000
27 Printing and publishing II 0.2 5,800,000
28 Chemicals and allied products II 3.6 5,800,000
29 Petroleum refining and related industries II 412.2 5,800,00
30 Rubber and miscellaneous plastic products II 18.6 5,800,000
31 Leather and leather products li 9.5 5,800,000
32 Stone, clay, glass, and-concrete products II 10.3 5,800,000
33 Primary metal industries II 17.8 5,800,000
34 Fabricated metal products n 1.2 5,800,000
35 Machinery, except electrical II 1.9 5,800,000
36 Electrical machinery and equipment li 1.1 5.800.000
37 Transportation equipment II 1.9 5,800,000
38 Measuring, analyzing, and controlling instruments II 6.8 5,800,000
_23 Miscellaneous manufacturing ll 1.5 5,800,000
Source: Colorado Energy Research Institute Report, Colorado Energy Consumption Patterns, prepared by
Browne, Brotz, and Coddington, 1980, using 1977 data.
* May be expressed in millions of Btu for ease of computation.


- -. ...... --- .......................-

A-l A B C x D = E > 7 = G
SIC Code Type of Industry Unit of Measure No. of Units Avg. Use per Unit ( MWH ) Estimated Usc- ( MWH ) Btu / MWH Total Btus
20 Food and kindred products Employee 33.6 3.413,non-
22 Textile mill products ll 0.8 , 3,413,000
23 Apparel and other textile products ll 1.5 3.413,000
24 Lumber and wood products ll 5.1 3,413.000
25 Furniture ancT fixtures II 1.3 3 j 413 j non
26 Paper ana allied products ll 29.6 3.413.000
27 Printing and publishing ll 5.2 3.413,000
28 Chemicals and allied products II 34.6 3.413.000
29 Petroleum refining and related industries ll 219.2 3.413.000
30 Rubber and miscellaneous plastic products ll 11.1 3.413.000
31 Leather and leather products ll 17.4 3,413,000.
32 Stone, clay, glass, and concrete products ll 18.3 3.413.000
33 Primary metal industries ll 53.0 3,413,000'
34 Fabricated metal products ll 25.4 3,413,000
35 Machinery, except electrical ll 7.4 3,413,000
36 Electrical machinery and equipment ll 3,413,000
37 Transportation equipment ll 15.2 3 j 413 j nnn .
38 Measuring, analyzing, and controlling instruments ll 1.7 _3,413,000
39 Miscellaneous manufacturing ll 5.5 3 ^413.000
Source: Colorado Energy Research Institute Report, Colorado Energy Consumption Patterns, prepared by
Browne, Brotz, and Coddington, 1980, using 1977 data for Denver Metro area.
* May be expressed in millions of Btu for ease of computation.

A-l - A B c. n ? F ( r,
SIC CODE Type of Industry Unit of Measure No. of Uni ts i Avg. Use1 .per-unit (Gallons) Estimated Use (Gallons) Btu/ Gallons Total Btu's
20 Food and kindred products
22 Textile mill products
23 Apparel and other textile products
24 Lumber and wood products
25 Furniture and fixtures i '
25 Paper and allied products employee 504.4 91,600
27 Printinq and publishing
28 Chemicals and alli'ed products employee 159.6 91,600
29 Petroleum refining and related industries employee 7,169.4 91,600
30 Rubber and miscellaneous plastic products
31 Leather and leather products
32 Stone, clay, glass, and concrete products
33 Primary metal industries
34 Fabricated metal products
35 Machinery, except electrical
36 Electrical machinery and equipment
37 Transportation equipment
38 Measuring, analyzing, and controlling instruments
39 Miscellaneous manufacturing
^Source: Colorado Energy Research Institute, Report, Colorado Energy Consumption Patterns, prepared by Browne, Brotz, and Coddington, 1980, using 1977 data.

I W it! 4jJ n j ^ ' f
A B + C + D + E + F + G H
Distil- Resid-
SIC Code Type of Industry Natural Gas Elec- tricity Coal late Fuel Oil ual Fueloil LP Gas Total
20 Food and kindred products
22 Textile mill products
23 Apparel and other textile products
24 Lumber and wood products
25 Furniture and fixtures
26 Paper and allied products '
27 Printing and publishing
28 Chemicals and allied products
29 Petroleum refining and related industries
30 Rubber and miscellaneous plastic products
31 Leather and leather products
32 Stone, clay, glass, and concrete products
33 Primary metal industries
34 Fabricated metal products
35 Machinery, except electrical
36 Electrical machinery and equipment
37 Transportation equipment
38 Measuring, analyzing, and controlling instruments
39 Miscellaneous manufacturing

20FOOD S- KI 606 33.6 20361 .6 103029.7 6.949E10 370.7
22TEXTILE M 0 .8 0 0 0 117
23APPAREL 8> 30 1.5 45 227.7 1 5359E8 15.6
24LOMEER & 13 5.1 91.8 464.508 3.1331E3 103.3
25FURNITURE 86 1 .3 111.8 565.708 3.8157E3 23.2
26PAPER S A 20 29.6 592 2995.52 2.0205E9 47 1.8
2 7PRI NTING 108 5.2 561.6 2841.696 1.9167E9 19.3
28CHEMICALS 55 34.6 1903 9629.18 6.4 9 4 9 E 9 '260.6
29PETR0LEUM 0 219.2 0 0 0 2773.9
30RUBBER & 44 11.1 438.4 2471.304 1.6 6 6 9 E 9 399.9
31 LEATHER S 0 17.4 0 0 0 98.7
32ST0NE,CLA 152 18.3 2781.6 14074.90 9.4936E9 593.6
33PHIMARY M 1 1 53 583 2949.98 1.9898E9 322.3
34FABRICATE 276 25.4 7010.4 35472.62 2.393E10 98.3
35MACHINERY 870 7.4 6438 32576.28 2.197E10 101.9
36ELECTRICA 1 1 4 9.6 1094.4 5537.664 3.7 3 5 2 E 9 61.1
37TRANSPORT 1 2 15.2 132.4 922.944 6.2253E8 43.9
38MEASURING 522 1 7 887.4 4490.244 3.0287E9 7.3
39NISC MANU 55 5.5 302.5 1530.65 1.0324E9 * 4 4.5*
43434.9 I.482E11

224644.2 718861.4 1.909E11 6.4 3878.4 155136 8.688E10 9.8
0 0 0 8.6!
468 1497.6 3.978E8 . 8 j
1859.4 5950.08 1.5305E9 . 1 1.8 72 40320000 7.3'
1995.2 6384.64 1.6 959E9 1.3^
9436 30195.2 8.0206E9 6.2 124 4960 2.7776E9 62.3
2084.4 6670.08 1.7717E9 . 5
14333 45365.6 1.218E10 1.8 99 3960 2.2176E9 24.9
0 0 0 14.6
17595.6 56305.92 1.496E10 5.5 242 9680 5.4203E9 13.8
0 0 0 1 0 0 0 9
90987.2 291159.0 7.734E10 56.5 3588 343520 1.924E11 6.7
3545.3 1 1344.96 3.0135E9 126.8 1394.8 55792 3.124E10 2.1
27130.8 86818.56 2.306 E10 .2 55.2 2208 1.2365E9 1 7
88653 283689.6 7.536E10 . 3 2 6 1 1 0440 5.S464E9 2
6965.4 22289.23 5.9206E9 . 3 34.2 1368 7.6608E8 1 .2
526.8 1685.76 4.4778ES 1 2 430 2.688E8 1 .4
3810.6 12193.92 3.2390E9 2.3
2447.5 7332 2.0804E9 -1 5.5 220 1 .232E8 1 6
1588744. 587836
496482.4 4.220E11 1 4695.9 3.292E11

5938.9 310302.3 3.559E10 9.3 5635.8 204579.5 3.269E10
0 0 0 6.5 0 0 0
24 1254 1 43 84 lift . 2 6 217.8 34800000
131.4 6865.65 7.8753E3 1 7 30.6 1110.78 1 7 7 4 8 E 3
111.8 5841.55 6.7006E8 . 4 7396 263474.8 4.290E10
12 4 6 65103.5 7.4 6 7 8 E 9 76 1520 551 76 8.816E9 504.4
54 232 1 .5 3.2 3 6 4 E 8 . 2 21.6 784.08 1.2 5 2 3 E 8
1369.5 71556.33 3.2080E9 3.6 198 7137.4 1.1434E? 159.6
0 0 0 4 12.2 0 0 0 7169.4
607.2 31726.2 3.6 3 9 2 E 9 18.6 318.4 29707.92 4.7467E9
0 0 0 9.5 0 0 0
1018.4 5 321 1 .4 6.1037E9 10.3 1565.6 5 683 1.28 9.0305E9
23.1 1206.975 1.3S45E8 17.3 195.8 7 1 07.54 1.1356E9
469.2 24515.7 2.8121E9 1 .2 331.2 12022.56 1 9 2 1 0 E 9
1740 909 1 5 1.043E10 1.9 16 53 60003.9 9.5874E9
136.8 7147.3 3.19 9 0 E 8 1 1 125.4 4552.02 7.2 7 3 2 E 3
16.3 377.3 1.0069E8 1 .9 22.3 327.64 1.3224E8
1200.6 62731.35 7 1 9 5 7 E 9 6.3 3549.6 128850.5 2.059E10
88 45 9 8 5.2742E8 1 5 3 2.5 2994.75 4.735E8
4175.6 740675.1 3.4 9 6 E1 0 28152.3 8 4 0 4 28.5 l.343E11

1491909. 4.156E11
0 0
3 19 7.1 7.3003E8
14463.02 2.3991E9
281266.7 4.564E10
5 0 4.4 10033 7162.48 9.2406E3 165592.7 3.003E10
13117.36 4.1374E9
159.6 3773 6232.38 3.0406E8 1 4 4 4 >0.9 3.106E10
7169.4 0 0 0 0 0
1298 > 1 3 3.0 4 3 E1 0
0 0
- 753796.6 2.9 4 4 E 1 1
7840 1.46 3.752E10
161037.4 5.296E10
4776. 4.8 1.232E11
4 0 89 4.76 1 1 9 7 E 1 0
4794.144 1 5 7 2 0 E 9
208266.0 3.4 0 5 E1 0
17175.4 4.241?E9
18866 3 9 9 0859. 1 1 20El 2
1 1 2 0 E1 2

4.3.5 Transportation
The worksheet for the transportation sector has been reproduced in a nearly identical form on the computer version. The only chanse is that daily sallons (in column L) are multiplied by weighted days (column M) before beins totaled by pupose. Total annual sallons then appears at the bottom of column N in position N9. Price per Sallon is inserted in Nil* and value of annual use appears in N13.
Althoush simplification of the worksheet was possible* it was left in its orisinal form for the computer version for two reasons. First* it is not a very Ions complex worksheet to besin with. More importantly* it was felt that a user should understand how the transportation sector data is developed? this is more apparent with all subtotals and calculations shown.
5 7

4.3.6 Government
The manner in which the Government sector is included in the workbook is curious. It would almost appear to be an afterthought. It is certainly not consistent with the remaining analysis! the scale of the analysis is entirely different* with actual utility billinSs required. Moreover* some of the units of analysis* e.s. schools* are also included in the commercial analysis* causing this energy use to be counted twice.
While government's analysis of its own energy use is certainly worthwhile* this worksheet is not an appropriate means. Although more complex than an analysis than the other sectors* it is far to simplified to be of actual use in analyzing government energy use. For example* vehicles are not broken down into diese1/saso1ine categories or weight categories. Certain government uses* such as fire stations* have been included* while others* such as police stations* have been ignored. Community street lighting* a huge part of the government utility bill* has been entirely forgotten. Futhermore* units of analysis are not suggested which allow comparison of energy consumption. This is a vital necessity if a government is to implement conservation measures.
The workbook analysis of government use is clearly unacceptable. Since every government will have different facilities* the government sector analysis will have to be
5 3

set up differently each time the analysis is made in a
different community. When this is doner the enersy consuming units should be presented in comparable form. That isr instead of showins that water treatment plant "A" uses a certain amount of electricityr show that water treatment plant "A" uses so much electricity per million sallons of water treated. This then allows comparison of treatment plant "A" to treatment plant "E" .
4.3.7 Community Enersy Use Summary
The workbook contains only one summary pass, it is for the tarSet year"r and intended to show differences between expected supply and demand. Curioushi current year community enersy use is never totaled. The computer summary file is a complete departure from the workbook. It was felt that communities would be interested in their current enersy consuption> as well as the cost of current enersy expenditures. Columns for tarset year user price and cost have been included as well.
Because prices of enersy vary by sector^ and because prices need be entered only once for each yearr a separate price column is included in the summary file. Future use may be entered as a percentase of current use or as a separate value. Each sector/fuel is entered independently, allowins for varied rates of srowth across sectors or inter-fuel substitutions.

Total Energy Use
Land Use Natural Gas (MCF) Electricity (MWH) L.P.G. (gallons) Coal (tons) Distillate Fuel Oil (gallons) Resi dual Fuel Oil (gallons) Gasoline (gallons) Other (gallons)
Supply Data
Difference Between Supply and Demand

ELEC 88612873 .0574 5086379. 1.3292E8 3 04 1 3 8 2 3 6 0 8
NAT GAS 21235503 .3481 7392079 . 31853255 0 6 5 2070462.
LPG 143189.7 . 7 100232.8 214784.6 34 7302675.
FUEL OIL 32957.71 1.12 36912.64 49436.57 4.5 222464.5
12615603 23419209
ELEC 10645099 .0574 61 1028.7 15967649 1 04 1660635.
NAT GAS 2574433 .3481 396160. 1 3861650. 065 251007.2
1507189. 1911643.
ELEC 1.265E8 .05 06 6400900 1.3975E8 1 04 1 9734000
NAT GAS 14587440 . 3 2 4667981 . 21881160 0 65 1422275.
11068331 21 156275
ELEC KWH 434349 .0 56 24323.54 651523.5 1 04 67758.44
NAT GAS C 4964824 n -* 4L 1533744. 7447236 065 48 4 0 70.3
COAL 1 46 9 5 40 587800 22042.5 5 6 123438 0
DFO 14175 52 737100 21262.5 3 4 722925
RFO 2 315 2 36 833472 34728 33 1146024
LPG 18 8 6 6 . 72 13583.52 28299 23 650377
4 3 0 6 0 3 5

GASOLINE 3322763
1.2 3987316. 4984145
1 7 =; £ 7 ? 7 ? 5 7
ELEC 0 . 0506 0 0 . 1 04 0
GAS 0 .0 32 0 776 . 0 6 5 50.44
0 5 0.44
TOTAL = 2.2619E8 2758.443 3 54 47 28
43362200 528.8073 169.2183

4.4 Selection of Community For Testing the Method
4.4.1 Eou1der
The City of Boulder was the first choice as the site for testing the method. Boulder participated in the C.C.E.M.P. program and as a result has completed a very detailed and extensive energy audit using another (Hittman) method. This would offer the opportunity to compare both data collection efforts and results. After further
consideration- however- it was concluded that Boulder would not be representative of a typical data collection effort. As the result of extensive effort- much of the input data required has been collected for the C.C.E.M.P. study. To simply apply this input to the O.E.C. audit technique would not reflect actual effort required. To re-create the data collection process as required by the O.E.C. workbook for 1973- the year Boulder conducted their study- was
considered unfeasible. Much information which is readily available for a current year is not available for a
previous year.
Another option would be to collect the data for Boulder for the current year in order to compare data
collection efforts- then use the 1973 input to compare
results. This course of action was rejected following an assessment of the Boulder Planning Department's resources-which indicated much greater sophistication than would be
typical of most communities of this size. Better than

average community planning data would bias the study of the
data collection effort. It was felt that data collection in Boulder would be easier than in most communities of this sizer and would not reflect typical data collection effort.
4.4.2 Arvada
The City of Arvada was selected as the site for data collection. A population and location close to those of Boulder's makes certain general comparisons possible. Arvada's planning resources are more typical of a city of this size. Arvada offers various challenges to the energy planning process because it is part of a metropolitan area rather than free-standinSr and because it is located in two counties. An additional factor in the selection of Arvada was the city's interest in energy planning and willingness to cooperate in the study.

5.0 Data Collection and Reliability
5.1 Collecting the Input Data
Because this workbook technique had not been used previously! the data collection effort was carefully documented. The intent is to show that an unreasonable demand on staff time is not required! and that sources of required data are readily available.
5.1.1 Residential
In the workbookr the residential sector is divided into two categories; single family and multi-family. Similar input is required for each.
From the community itself only the number of units is required. The Arvada Planning Department keeps track of the number of housing units in the following manner; a large map of the city is divided into about forty sections. In each section the number of both single family and multi-family units is shown. These figures are updated constantly as building permits are issued.
About fifteen minutes was required to add the number of housing units and to re-check the figures once.
There are two other sets of data required for the residenial sector. The first is "percent of units using
fuel type".
For exampler how many homes use gas versus

electric for heat or water heating. Also included in this column is market penetration? for exampier the percentaSe of homes with color televisions. A related set of data is the average amount of fuel consumed by each ot the equipment or-appliance types.
The workbook suppliedr as an exampler both sets of data for the Denver metropolitan area. Since this includes Arvadar there was no need to replace these figures. Eut in order to accurately reflect the effort involved in obtaining this information, the process of acquiring it was repeated. checking at the same time to see if it had been updated
The census office was contacted and asked if this information has been collected more recently. Although it has been collected. in the 1930 census, it will not be publised until Fall of 1982. This office also confirmed that the smallest geographic unit at which average figures are provided is the entire Denver metropolitan area, including the non-urban parts of the counties. With access to the census computer tapes. data for Just Jefferson County could be obtained. Since the county is not very homogenous, this would hardly be worth the cost and effort.
The census office offered to mail a photo copy of the existing figures. The time required to obtain this data consised of a five minute phone call plus two or three days waiting for the printed copy to arrive in the mail.
The Public Service Company of Colorado was next contacted to determine if the data in the OEC workbook was

the most current available. Rodney McLenon, of the market resea rc h department, indicated that the company had data which was not only more recent (1931 vs. 1979) but also more seosraphicly specific, encompassing the cities of Arvada, Westminster and WheatridSe.
Although it took two weeks to have my call returned (Mr. McLenon had been on vacation) and another ten days to recieve the data through the mail) the actual working time involved in obtaining this data was about twenty minutes.
The entire data collection process for the residential sector was completed in three weeks, and required about forty-five minutes of actual working time.
5.1.2 Comme rc i a 1
The commercial sector involves the most complex data collection. Information on thirteen uses is required. Four categories were eliminated because there are no such facilities in Arvada! ski facilities, hospitals, colleges, and enclosed malls.
Much of the remaining input required is in the form of square feet of building area. The Arvada Planning Department was contacted initially. They indicated that they had no summary information of this type. The only available summary of uses is by land area of zoning categories, but this data is not thought to be highly accurate. Even if it were accurate, there is no reliable means of determining building coverage as a percentage of

lot size
The only means the PlanninS Department could suggest to obtain the data on square footase was an elaborate three step process. The c omrne rc i a 11 y z on ed land is shown on a series of about twenty larse maps of the city. In case of recent development> subdivision or project names are given on the maps. One can look up the names in a file, but unfortunately this file is chrono1 oslca1 rather than alphabetical. This file provides a filing number which corresponds to a set of plans (filed in two different parts of the city building) which would likely give the square footage of the building.
For several reasons. this method of data collection was rejected. The maps were not considered to be highly accurate. Developments consistent with zoning and older developments did not have filed plans, primarily required for residential subdivisions and F.U.D's. Even if gross square footaSe was obtained. for shopping centers in particular, it would not provide required detail on uses.
The Arvada Building Department was consulted to see if they could provide more accurate information. They indicated that it would be impossible to obtain the needed information from their records.
The city's sales tax office was contacted next. This office maintains excellent, up-to-date and highly specific information on businesses in the city. Unfortunately, this does not include square footage of the businesses. Several pages of business listings were collected, particularly for

specific use categories such as restaurants, food stores
and motels. These listings were not so extensive that they could not all be contacted individually.
The sales tax office also lists the major shoppinS centers and 1 a rSe r office buildinSs. These lists were obtained with the intention of cross-referencina with planning records. It soon became apparent that this would not work} project names used durins the development process do not necessarily correspond to current business names.
Input for the' hotel/motel category! the number of rooms per estab1ishment, was obtained by phone. Number of employees for "recreation other than skiins" was obtained in the same manner. The larder food stores were contacted by phone and save the necessary information of ssuare footage.
Since the number of "eatins and drinkins establishments" was not too large to contact individually, this was attempted. Twelve calls produced only one manager able to provide the sauare footage of his restaurant, and he said it was just a guess. The method of phone calls to restaurants was abandoned as a means of obtaininS this info rma t i on.
The Arvada Chamber of Commerce was contacted, but they had no information about commercial ssuare footase in the city.
Since the private sector often maintains Sood data for uses such as market studies, Lawrence Hamilton, a private
developer of shopping centers and office buildinSs was

contacted. Mr. Hamilton suggested contacting the Denver Chamber of Commerce and the real estate firm of Fuller and Co.r both of which survey office space regularly. He also suggested looking at the Arvada Urban Renewal Authority project market study.
The Denver Chamber of Commerce has totals for office spacer but it is for the entire northwest auadrant of the Denver metropolitan area. They did offer to send a shopping center directory which they thought might be useful .
Fuller and Co. has office space totals for a smaller geographic arear but one which included more than just Arvada. Fuller and Co. has square footage of individual buildings? it was possible that they could make this information available if Arvada buildings were identified.
The Arvada Urban Renewal Authority market study was obtained. The purpose of this study was to determine the demand for certain commercial and residential uses in a proposed urban renewal area in the city. The study was based soley on projected demands however? no data was provided on existing uses.
The Denver Regional Council of Governmentsr Gerry Allen of the University of Colorado Eusiness Research Division and the Census User's office were all contacted for help. None could offer any ideas for obtaining the necessary information on the commercial sector.
Several business directories^ including "Folk";
Contacts Influential
were examined to

what information on commercial uses was
available. None provided square footase, but "Contacts Influential" does provide a list of street addresses of all businesses by city. One could then look, up each address in the county tax records.
At this point the Jefferson County Tax Assessor's office was contacted. They pointed out that the information could be obtained much more easily by running a computer sort of their tax records. Not only could they provide a listing of the square footage of commercial usesi buti with forty-one use categories, they could provide the information sorted according to the categories required for the workbook. The charge for the print-out would be about one hundred dollars and could be available within one week. The only means of acquiring this information without paying for the computer generated print-outi I was told, would require first identifying the addresses of all commercial sites, then looking them up one file at a time. Ten days later, having raised the necessary funds, I placed another call to the assessor's office to order the print-out. This time I reached the person who is regularly in charge of commercial assessment records? he had been on vacation when I called earlier. He suggested that I could save the hundred dollar cost by looking up the information in a master print-out of all Jefferson County commercial property. This book is available for public use in the assessor's office. I took
this advice.

properties are listed by street
A1 1
address. One only has to look at addresses within the extreme north and south boundaries of the city. Due to irregular borders; some properties within the boundaries will not be in Arvada. About twenty numerical codes identify properties within Arvada. If the property is shown by the code to be within Arvada; the property use code is checked next. Apartment buildings are included in the assessor's commercial category; but are not included in the OEC workbook commercial category (they appear in multi-family residential). All non residentia 1 commercial properties were copied onto a list which included address; use; and square footage of building.
About seven hours was retired to copy this
information; and another four hours to sort it into the workbook categories; add and re-check the figures.
A small portion of Arvada is within Adams County. The assessor does not keep their records in the same manner as Jefferson County. However; they do have very large aerial photographs; six of which cover the portion of Arvada in Adams County. The maps were examined; and any property which appeared to have a possible commercial use; was noted. The assessor's files for these properties were examined. The use and building square footage for the commercial properties was listed.
Obtaining the commercial data from Adams County required about two hours.
The final input for the commercial sector is value of

annual construction? in millions of dollars. Since this figure is published monthly in "Colorado Business Report"? the C. U. Business Research Division? which publishes this periodical? was contacted. They were able to provide the 1931 total.
5.1.3 Industrial
The input required for the industrial sector is the number of employees? by two-digit S.I.C code. A call to the census bureau produced the information that the smallest geographic unit for which this data is available is by county. This is of no use in Arvada? particularly because this city is located in two counties.
The U.S. Department of Labor's local office was contacted next in the expectation that they .might collect their own data on employment. They do not? but suggested I call the state.
After three calls to the state? I was able to reach the appropriate person. Unfortunately? to no avail; the state does not collect this information by city either. This person thought the Denver Regional Council of Governments had completed a localized study of industrial employment? he gave me the name of a person to contact at D R C 0 G .
The D.R.C.O.G. contact was on vacation. When he returned? ten days later? he explained that their study concerned industrial land area? not employment.

The Arvada Planning Department had no information on
industrial employments but suggested contacting the Arvada Chamber of Commerce. The Chamber of Commerce had no informations but suggested using the directory "Contacts Influential".
While searching for "Contacts Influential" on the library shelf. another book caught my eye! "The Directory of Colorado Manufacturers". Both list manufacturers by S.I.C. code. The Colorado directory had a separate listing for Arvadas the other listed all Denver area manufacturers alphabetically. Each describes employment by size cateaoriess but each uses a different set of size categories. By listing both categories for each manufacturers the number .of employees could be more specificly pinpointed. The median figure within the resulting category was the one used in totalling employment by S.I.C. code.
The data collection for the industrial sector covered a span of about three weekss with about five hours of actual working time required.
5.1.4 Transportation
Because the only input required for the transportation sector is the number of residential unitss collected previously for the residential sector, no additional time was required for the transportation sector.

5.1.5 Government
As discussed in section 4.3.6. the O.E.C. worksheet for the government sector was felt to be incomplete and would require adaptation for each local use. In Arvada information on the city's energy use is regularly maintained by the energy director. This might not be available for the typical city> so the time required to assemble this data was included in the data collection calculation. Since the computer file must be set up to accomodate the input from each cityf this time was included as part of the time and effort calculation as well.
The total time required for the government sector data collection was six hours.
5.1.6 Energy and Fuel Prices
The Public Service Company was contacted for information on average prices for electricity and natural gas. These prices have two components? a fixed rate set by the Public Utilities Commission, and a fuel cost adjustment which varies monthly according to the price P.3.C. pays for fuel. P.S.C estimates the average annual price per
kilowatt hour and hundred cubic feet of natural gas. and uses these estimates for their "budget billing customers", whose annual fuel bills are averaged across twelve months. The current average price was used in this analysis of cost
of energy used. There are actually several different

residential and commercial rates
but for this analysis
only one residential averase and one commercial averase were utilized.
Public Service Company provided same other useful information! total electricity and natural 3as supplied to Arvada in 1931. and estimated future price increases. The total supply is used to verify the reasonableness of total arrived at through the O.E.C. analysis. The future prices are used to estimate energy costs based on future energy use .
A number of government agencies were contacted in the expectation that one would keep track of average fuel prices in Colorado. Offices contacted included the Public Utilities Commission! the Oil and Gas Conservation Commission! the Department of Natural Resources! the Office of Enersy Conservation! the federal Department of Energy! and the Colorado Energy Research Institute. None of these agencies had the price information! all suggested I call one of the others. Turning to the private sector! I called two trade associations! the Colorado Petroleum Association and the Colorado Petroleum Marketing Association. The latter provided a national wholesale price for one fuel! but not for the others.
Finally. I simply called local suppliers listed in the yellow pages. I was able to obtain several residential and commercial prices for propane and distillate fuel. The prices did not vary significantly and I simply used the
average in the fuel cost analysis.

None of the
local suppliers provided residual fuel
oil. To obtain a price for thisr I contacted a local refinery.
5.1.7. Summary of Data Collection
A total of twenty-six hours was required for the data collection process. The data collection took, place over the course of six weeks> although this could have been shortened. Included in these totals are hours spent trying to find the right sources or contacts^ and time spent collecting data that was not used in the analysisr because a better source was found. This data collection process required far fewer man-hours than the audits performed by the C.C.E.M.F. cities.

Modifications and Problems in Data
Collection* Including Rec arnmen da t ions for Verification
5.2.1 General Problems and Considerations
One problem with this analysis is that not all data is available for a single year. Some input* such as the number of homes* or fuel prices is current as of July* 1932. Some input is for the year 1981* such as value of construction. Certain average use figures are older* as old as 1977* for the industrial sector.
Geographic units of analysis used for average demand are not limited to the City of Arvada. In some cases the averages are for the area surrounding the city* for the entire Denver metropolitan region* or even for the state as a whole.
Like any analysis using vast amounts of data from various sources* this one is bound to include errors made in the primary data collection* typing and reproduction. Since some of this data is third or fourth hand* the opportunity for error increases. One hopes that major errors will simply look funny. An example is a listing in the assessor's records which showed a fast food restaurant with a building area of 12*425 sg.ft. Since this is ten times the size of the average fast food restaurant* I asked to see the original card on the property. Sure enough* an extra digit had been inserted when the information was

coded into the computer. The best means of eliminating gross errors of this sort is to develop reasonable parameters for individual pieces of inputs and to develop means of cross-checking larger totals.
5.2.2 Residential
I have assumed that the Arvada Planning Department keeps accurate records on the number of housing units and thus made no attempt to check these figures. If one lacked my confidence in the Planning departrnentr a means of checking would be to add the number of building permits issued since the 1930 census was taken to the total housing figures from the 1930 census.
A fairly serious problem in the residential data is the distinction between "single-family" and multi-family" designations. The city's definition of "mu 11i-fami1y" does not co-incide with the workbook definition. The workbook includes tri-plexs and four-plexes (as well as duplexes and mobile homes) in the "single-family" category. Like most cities* Arvada considers attached units* such as four unit town homes, as mu 11i-fami 1y. There being no way to readily identify all tri- and four-plexes* or even estimate the percentage of them* this input data was used as is. It is understood that this will bias the residential consumption figures somewhat* resulting in lower use than would otherwise be shown.
The appliance
or equipment shares data provided by