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. Liquid fuel is one of the primary energy source for these FOBs, which is a non-renewable source, flammable, and needs large convoys with specialized equipment to transport, therefore being both unsafe and expensive for the operation of FOBs. 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, occupancy, and other relevant parameters of interest, and upload data points into a database. The modeling framework is developed using the programming language Ruby and simulation platforms OpenStudio and EnergyPlus. In order to make sure the ERPT estimates reasonably accurate load profiles for a shelter through regression techniques, a large set of data points, on the order of around 500,000 data points, needs to be uploaded into the database. The database is named DEnCity and is established using Amazon Web Services (AWS). This research developed programmatic workflow to perform Sensitivity analyses along with Sampling and Uncertainty analyses to generate and upload the data points needed into the DEnCity database. It analyzes different Sensitivity and Uncertainty methods for creating and uploading data points. It compares these computational methods and discusses their pros and cons in context of the ...