Energy Equity, Infrastructure and Demographic Analysis with XAI Methods
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911165225893888 |
|---|---|
| author | Shrestha, Sarahana Varde, Aparna S. Lal, Pankaj |
| author_facet | Shrestha, Sarahana Varde, Aparna S. Lal, Pankaj |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16279 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy Equity, Infrastructure and Demographic Analysis with XAI Methods Shrestha, Sarahana Varde, Aparna S. Lal, Pankaj Computers and Society Artificial Intelligence I.2.6; H.4.2 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. |
| title | Energy Equity, Infrastructure and Demographic Analysis with XAI Methods |
| topic | Computers and Society Artificial Intelligence I.2.6; H.4.2 |
| url | https://arxiv.org/abs/2509.16279 |