8B

- i APPENDIX 8B. UNCERTAINTY AND VARI ABILITY IN LCC ANALY SIS

TABLE OF CO NTENTS

8B.1 INTRODUCTION

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8B.2 UNCERTAINTY

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8B.3 VARIABILITY

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8B.4 APPROACHES TO UNCERT AINTY AND VARIABILIT Y

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8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYST AL BALL

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LIST OF FIGURES

Figure 8B.5.1 Normal, Triangular, Uniform, Weibull, and Custom Probability

Distributions

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- 1 APPENDIX 8B. UNCERTAINTY AND VARI ABILITY IN LCC ANALYSIS

8B.1 INTRODUCTION

Analysis of energy conservation standards involves calculations of impacts, for example, the impact of a standard on customer life

- cycle cost (LCC). In order to perform the calculation, the analyst must first: 1) specify the equation or model that will be used; 2) define the quantities in the equation; and 3) provide numerical values for each quantity. In the simplest case, the equation is unambiguous (contains all relevant quantities and no others), each quantity has a single numerical value, and the calculation results in a single value. However, unambiguity and precision are rarely the case. In almost all cases, the model

, the numerical values , or both for each quantity in the model are not completely known ( i.e., there is uncertainty) or th e model

, the

numerical values

, or both for each quantity in the model depend upon other conditions ( i.e., there is variability).

Thorough analysis involves accounting for uncertainty and variability. While the simplest analysis involves a single numerica l value for each quantity in a calculation, arguments can arise about what the appropriate value is for each quantity. Explicit analysis of uncertainty and variability is intended to provide more complete information to the decision

- making process.

8B.2 UNCERTA IN TY

When making observations of past events or speculating about the future, imperfect knowledge is the rule rather than the exception. For example, the energy actually consumed by a particular appliance type (such as the average refrigerated beverage ve nding machine ) is not directly recorded, but rather estimated based upon available information. Even direct laboratory measurements have some margin of error. When estimating numerical values expected for quantities at some future date, the exact outcome i s rarely known in advance.

8B.3 VARIABILITY

Variability means that different applications or situations produce different numerical values when calculating a quantity. Specifying an exact value for a quantity may be difficult

, because the value depends on some thing else. For example, water heater energy consumption depends upon the specific circumstances and behaviors of the occupants ( e.g., number of persons, length and temperature of showers , etc.). Variability makes specifying an appropriate population value more difficult in as much as any one value may not be representative of the entire population. Surveys can be helpful here, and analysis of surveys can relate the variable of interest ( e.g., hours of use) to other variables that are better known or easier to forecast ( e.g.,

persons per building ).8B

- 2 8B.4 APPROACHES TO UNCERT AINTY AND VARIABILIT Y

This section describes two approaches to uncertainty and variability:

• scenario analysis, and

• probability analysis.

Scenario analysis uses a single numerical value for each quantity in a calculation, then changes one (or more) of the numerical values and repeats the calculation. A number of calculations are done, which provide some indication of the extent to which the result depends upon the assumptions. For example, the life

- cycle cost of an appliance could be calculated for energy rates of 2, 8, and 14¢ per kWh.

The advantages of scenario analysis are that each calculation is simple; a range of

estimates is used an d crossover points can be identified. (An example of a crossover point is the energy rate above which the life

- cycle cost is reduced, holding all other inputs constant. That is, the crossover point is the energy rate at which the customer achieves savings in operating expense that more than compensate for the increased purchase expense.) The disadvantage of scenario analysis is that there is no information about the likelihood of each scenario.

Probability analysis considers the probabilities within a ran ge of values. For quantities with variability ( e.g., electricity rates for different commercial enterprises ), surveys can be used to generate a frequency distribution of numerical values ( e.g., the number of commercial enterprises with electricity rates at particular levels) to estimate the probability of each value. For quantities with uncertainty, statistical or subjective measures can be used to provide probabilities ( e.g., manufacturing cost to improve energy efficiency to some level may be estimated to be $10 ± $3).

The major disadvantage of the probability approach is that it requires more information, namely information about the shapes and magnitudes of the variability and uncertainty of each quantity. The advantage of the probability approach is that it provides greater information about the outcome of the calculations, that is, it provides the probability that the outcome will be in a particular range.

Scenario and probability analysis provide some indication of the robustness of the policy gi ven the uncertainties and variability. A policy is robust when the impacts are acceptable over a wide range of possible conditions.

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYST AL BALL

To quantify the uncertainty and variability that exist in inputs to the e ngineering, LCC, and payback period (PBP) analyses, the U.S. Department of Energy ( DOE ) used Microsoft Excel spreadsheets combined with Crystal Ball, a commercially available add

- in, to conduct8B

- 3 NORMAL UNIFORM TRIANGULAR

WEIBULL CUSTOM probability analyses. The probability analyses used Monte Carlo simulation and probability

distributions.

Simulation refers to any analytical method meant to imitate a real

- life system, especially when other analyses are too mathematically complex or too difficult to reproduce. Without the aid of simulation, a spreadsheet model will only reveal a single outcome, generally the most likely or average scenario. Spreadsheet risk analysis uses both a spreadsheet model and simulation to automatically analyze the effect of varying inputs on outputs of the modeled

syste m. One type of spreadsheet simulation is Monte Carlo simulation, which randomly generates values for uncertain variables again and again to simulate a model. Monte Carlo simulation was named for Monte Carlo, Monaco, where the primary attractions are casino s containing games of chance. Games of chance

, such as roulette wheels, dice, and slot machines, exhibit random behavior. The random behavior in games of chance is similar to how Monte

Carlo simulation selects variable values at random to simulate a model. When you r oll a die, you

know that a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular roll. It's the same with the variables that have a known range of values but an uncertain value for any particular time or event ( e.g., equ ipment lifetime, discount rate, and installation cost).

For each uncertain variable (one that has a range of possible values), possible values are defined with a probability distribution. The type of distribution selected is based on the

conditions surr ounding that variable. Probability distribution types include:

Figure 8B.5.1 Normal, Triangular, Uniform, Weibull, and Custom Probability Distributions

During a simulation, multiple scenarios of a model are calculated by repeatedly sampling values from the probability distributions for the uncertain variables and using those values for the cell. Crystal Ball simulations can consist of as many trials (or s cenarios) as desired

— hundreds or8B

- 4 even thousands. During a single trial, Crystal Ball randomly selects a value from the defined possibilities (the range and shape of the probability distribution) for each uncertain variable and then recalculates the spreads heet.