Abstract
In recent times, various efforts have been made to address the challenge of adequately representing
hydropower systems in modeling frameworks, accounting for the lack of data to represent the multiple
constraints in hydropower operation. This research is a pilot data-driven methodology for characterizing,
classifying, and comparing the water-to-energy and energy-to-water signal transformations that
hydropower facilities as signal processors accomplish. In this study, a Box Jenkins transfer
function/noise model is used to identify the relationship between reservoir inflows and outflows. For
examining the feasibility of this methodology, 5-minute fleet data for five storage and five run-of-river
facilities was provided by the Tennessee Valley Authority (TVA) and transfer function models are
developed. The influence of past inflow and outflow values on the current outflow decisions was
investigated and summarized by examining the results of Box Jenkins methodology. Finally, dominance
analysis was introduced to add value to the Box Jenkins model results and provide different stakeholders
with a set of concepts to convey the functionality of hydropower.
