Energy and environmental models are often complex. Sensitivity analysis is a natural choice to improve understanding of such models and to see which input parameters have the most influence on the output. It also helps analysts to understand the inner workings of a model and refine it accordingly. Examples range from the design of buildings for energy conservation to the environmental impact of oil spills. By better identifying the sources of uncertainty, changes in data sampling methods may also be made in order to create a more robust model.
Sensitivity analysis in environmental modelsEnvironmental parameters, depending on context, include wind, humidity, cloud cover, rainfall, currents, tides, temperatures and solar exposition. Sensitivity analysis can help in picking the most influential parameters to be included in a model, while leaving aside those that are unimportant. Given the difficulty of estimating values for some environmental parameters, it is useful to understand whether or not time and effort should be spent in trying to integrate them into the model. By concentrating on fewer, but more relevant parameters, model size can be reduced, simulation times can be shortened and better output obtained.
Sensitivity analysis in energy modelsEnergy and environment are typically linked. A model for energy will typically include output on factors such as carbon emission such as in the Belgrade Domestic Energy Model. This model uses sensitivity analysis to study differences in housing of different types and different ages. It also recognizes that complexity is still part of the model make-up because of the difficulty in assessing the behavior and interaction of a number of the input parameters. Other national energy models use physical laws, information on competition between energy sources, network effects (how the attractiveness of an energy is determined by the extent to which it is used), government policies and likely investment to model outcomes. Each factor can then be assessed for sensitivity as regards the final result.
Energy and environmental model methodsDefinitions of how models will be constructed in terms of information sampling and analysis of sensitivity and uncertainty (the overall measure of the degree of confidence possible in a model) vary. Possibilities include:
- Sampling. Monte Carlo, Quasi-Monte Carlo (deterministic), and Latin Hypercube.
- Sensitivity. Elementary effects/Morris method, ANOVA
- Uncertainty. Standard deviation, variance, covariance