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.
