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An energy systems modeling tool must address variability of ecological and socio-economic sensitivities in order to practically guide policy and budget related decisions. The aim of this effort is to produce an advisory and design tool geared toward aiding entities with robust planning of effective renewable energy solutions based on trusted models. A tool was developed that enables tradeoffs between various energy systems, based on neural network surrogate models of a publicly available power systems modeling tool. These surrogate models enable the higher-level decision-making tool to manipulate surrogate representations of actual engineering models, as opposed to relying on static data or static simulation results. This research will present a decision-maker with the ability to determine which various renewable and nonrenewable energy systems meet annual energy load requirements, acquisition and operation costs, and individual solution attributes.