Uncertainty and variability are related concepts. Variability generally refers to the distribution (range and other statistical characteristics) of input variables that researchers can estimate from surveys or other sampling analysis. Uncertainty refers to the assumed distribution of variables that researchers cannot easily sample, such as actual (in the field) equipment lifetime of a new technology. Previous analyses used sensitivity analysis to estimate the effects of these variabilities and uncertainties, using high and low values for specific inputs to bound their likely variation. The current approach uses uncertainty analysis, which systematically addresses all the variabilities and uncertainties, rank-orders their importance, and calculates the probability that life-cycle costs will be reduced in a transition to a new technology.

The importance analysis is often the first step in the uncertainty analysis process. This analysis ranks the inputs to the LCC or PBP equation in terms of their impacts on the results. The most important variables are included in the analysis, while variables that are uncertain but will not significantly affect the result are not researched further.

This approach to economic evaluation of energy-efficient technology uses probabilistic simulation. Combining this simulation with national energy consumption survey data provides the distribution of likely economic impacts on individual consumers. In other words, the results include not just the average impact, but the full range of impacts and the number of applications (e.g., households or commercial buildings) having net benefits, those having net costs, and the monetary value of each.

The LCC and PBP analyses use spreadsheet models developed in Microsoft Excel for Windows, combined with Crystal Ballâ„¢ (a commercially available software program). The models use a Monte Carlo simulation to perform the analysis accounting for uncertainty and variability. The spreadsheets are organized so that ranges (or distributions) can be entered for each input variable needed to perform the calculations.

For a detailed discussion of the methodology used to perform an LCC analysis, see, for example, Chapter 9 of the 2000 Technical Support Document for the energy conservation standard for water heaters.

Publications

• Lutz, J. et al., Life-Cycle Cost Analysis Of Energy Efficiency Design Options For Residential Furnaces And Boilers, Lawrence Berkeley National Laboratory. Berkeley, CA, January 2004. LBNL-53950. [PDF]
• McMahon, J. and X. Liu., Variability of Consumer Impacts from Energy Efficiency Standards, In Proceedings of the Second International Conference on Energy Efficiency in Household Appliances and Lighting, Naples, Italy, September 27 - 29, 2000. LBNL-45819. [PDF]
• McMahon, J., et al., Uncertainty and Sensitivity Analysis of Ballast Life-Cycle Cost and Payback Periods, Lawrence Berkeley National Laboratory. Berkeley, CA 94706, June 2000. LBNL-44450. [PDF]
• Lutz, J. et al., A Monte Carlo Approach to the Calculation of Energy Consumption for Residential Gas-Fired Water Heaters. In 2000 ASHRAE Annual Meeting; Minneapolis, MN; June 24 - 28, 2000. American Society of Heating Refrigeration and Air-Conditioning Engineers, Inc. 2000. LBNL-44829. [PDF]
• McMahon, J. and X. Xi, Uncertainty Analysis of Life-Cycle Cost: Residential Electric Heat Pump Water Heaters, In Proceedings of the ACEEE 1994 Summer Study on Energy Efficiency in Buildings, August 28 - September 3, 1994. American Council for an Energy-Efficient Economy, Washington, DC. 1994. [PDF]