用可解释AI分析用电负担，找出影响因素并设计公平性建议工具。
Energy Equity, Infrastructure and Demographic Analysis with XAI Methods
- 结合决策树与皮尔逊相关系数，识别影响用电负担的关键社会特征。
- 发现低收入家庭用电支出占收入比显著高于平均水平，存在明显不平等。
- 开发可交互的能源公平门户与计算器，适合政策制定者和社区组织使用。
本研究采用可解释人工智能（XAI）方法，如决策树和皮尔逊相关系数（PCC），分析多个地区的电力使用情况。聚焦能源负担问题——即家庭总能源支出占中位数收入的比例。通过整合社会人口统计学数据与能源特征，利用决策树和PCC识别出影响能源负担的关键因素，并提供可解释的预测指标。基于分析结果，设计了一个试点能源公平网络门户及新型能源负担计算器。该系统借助XAI技术，为多类能源利益相关方提供定制化、可操作的建议。研究旨在通过适配的XAI方法推动能源公平，实现更具包容性的能源政策制定与干预。
原文摘要 · Abstract (English)
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.
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