Energy Equity, Infrastructure and Demographic Analysis with XAI Methods
It addresses energy equity for stakeholders, but is incremental as it applies existing XAI methods to a new domain.
The study tackled energy burden by analyzing electricity usage and socio-demographic data using explainable AI methods, resulting in a pilot web portal and energy burden calculator for tailored advice.
This study deploys methods in explainable artificial intelligence (XAI), e.g. decision trees and Pearson's correlation coefficient (PCC), to investigate electricity usage in multiple locales. It addresses the vital issue of energy burden, i.e. total amount spent on energy divided by median household income. Socio-demographic data is analyzed with energy features, especially using decision trees and PCC, providing explainable predictors on factors affecting energy burden. Based on the results of the analysis, a pilot energy equity web portal is designed along with a novel energy burden calculator. Leveraging XAI, this portal (with its calculator) serves as a prototype information system that can offer tailored actionable advice to multiple energy stakeholders. The ultimate goal of this study is to promote greater energy equity through the adaptation of XAI methods for energy-related analysis with suitable recommendations.