IECM 12.0 beta User Manual > Introduction to Uncertainty Analysis >

Introduction

Nearly all analyses of energy and environmental control technologies involve uncertainties. The most common approach to handling uncertainties is either to ignore them or to use simple sensitivity analysis. In sensitivity analysis, the value of one or a few model input parameters are varied, usually from low to high values, and the effect on a model output parameter is observed. Meanwhile, all other model parameters are held at their nominal values. In practical problems with many input variables which may be uncertain, the combinatorial explosion of possible sensitivity scenarios (e.g., one variable "high," another "low," and so on) becomes unmanageable. Furthermore, sensitivity analysis provides no insight into the likelihood of obtaining any particular result.

A more robust approach is incorporated in the IECM to represent uncertainties in model parameters using probability distributions. Using probabilistic simulation techniques, uncertainties in any number of model input parameters can be propagated through the model simultaneously to determine their combined effect on model outputs. The result of a probabilistic simulation includes both the possible range of values for model output parameters and information about the likelihood of obtaining various results. You may have seen probabilistic analysis referred to elsewhere as "range estimating" or "risk assessment."

The development of ranges and probability distributions for model input parameters can be based either on statistical data analysis and/or engineering judgments. The approaches to developing probability distributions for model parameters are similar in many ways to the approach you might take to pick a single "best guess" number for deterministic (point-estimate) analysis, or to select a range of values to use in sensitivity analysis.