SUMMARY

The military is facing a significant issue of increasing fuel cost to deploy troops overseas and establish and maintain Forward Operating Bases (FOBs) or outposts. To help reduce energy consumption, transportation, and cost an Energy Resource Planning Tool (ERPT) is needed. This ERPT will help the military in making crucial decisions about the optimal shelter and equipment configuration for their Forward Operating Bases (FOBs) prior to deployment. To make this tool effective, load profile data of shelters needs to be simulated and uploaded into a database, so that it can be easily available when outposts need to be configured and optimized with respect to energy consumption for a given set of constraints.
This research has developed a programmatic modeling framework to generate load profiles for shelters of interest for outposts for different weather profiles, equipment, and other relevant parameters of interest, and upload data points into a database. In order to make sure the ERPT estimates reasonably accurate load profiles for a shelter through regression techniques around 500,000 data points need to be uploaded. This research developed programmatic workflow to perform Sensitivity analyses along with Sampling analyses to generate and upload the data points needed into the database. It analyzes different Sensitivity methods for creating and uploading data points. It compares these computational methods in context of the load generation profile for shelters used in FOBs.
This thesis provides details of the creation of the programmatic workflows and uses generated data to evaluate the analysis methods finally selected to create and upload data points to the DEnCity database. This research will enable the simulation of large number of data points corresponding to different shelters of interest, making it a valuable tool for providing the necessary inputs for an energy efficient design of Forward Operating Bases.