A Study on Detection Model for Energy Poverty Households using Smart Meter Big Data Training

Abstract

As the intensity of heatwaves and cold waves escalates due to the climate crisis, energy is being redefined as a necessity for survival rather than a mere commodity. However, the current income-based energy welfare system has limitations in detecting 'behavioral energy poverty' households that voluntarily forgo cooling and heating due to economic burdens. Therefore, this study proposes a 'Virtual Sensing' model capable of identifying potential poverty groups solely based on household electricity consumption patterns using established smart meter (AMI) big data, without requiring income information. This study conducted a 'Dual-Track Framework' combining micro-level modeling and macro-level verification. In Phase 1, an unsupervised clustering model based on temperature sensitivity (β) was constructed using high-resolution household data from Tokyo. In Phase 2, the social validity of the model was verified by fusing heterogeneous big data (electricity, weather, poverty) from Seoul. The major findings are as follows. First, the micro-analysis identified a 'Flat Pattern' cluster where electricity consumption hardly increased despite rising temperatures (β ≈ 9.03\%). This cluster (Cluster 2) remained at a minimal consumption level with an average electricity consumption of 70.3 kWh, and the average number of household members was 1.37, showing patterns similar to the characteristics of single-person vulnerable households. Second, the macro-verification revealed a distinct negative correlation (𝑅 ≈ −0.53) between summer electricity sensitivity and regional poverty rates, showing significant associations with housing benefit rates and the ratio of elderly living alone. In particular, this study elucidated 'Seasonal Asymmetry,' where summer electricity patterns (cooling as a luxury good) reveal consumption disparities based on income levels more clearly than winter patterns (heating as a necessity). This study empirically demonstrated the feasibility of detecting hidden poverty groups through a data- driven methodology despite privacy constraints. The proposed model is expected to contribute to the realization of 'Active Welfare' based on data, such as real-time monitoring of households in crisis by local governments and precision targeting of energy vouchers. Keywords: Behavioral Energy Poverty, Smart Meter (AMI), Virtual Sensing, Temperature Sensitivity, Unsupervised Clustering, Seasonal Asymmetry