Operations Research 2025
Abstract Submission

2379. Smart Meter Analytics and Tariff Design for Alleviating Energy Poverty: Evidence from a Clustering-Based Study in Montreal

Invited abstract in session WC-3: Energy planning and policy, stream Energy and Sustainability.

Wednesday, 13:30-15:00
Room: H5

Authors (first author is the speaker)

1. Rahil dejkam
School of Business and Economics / E.ON Energy Research Center, RWTH Aachen University
2. Runming Jia
Georesources and Materials Engineering, RWTH Achen University
3. Reinhard Madlener
School of Business and Economics / E.ON Energy Research Center, RWTH Aachen University

Abstract

Energy poverty, defined as the inability to afford adequate energy services, poses serious health and comfort risks, particularly in regions with extreme climates. Traditional identification methods often rely on static income-based indicators, failing to capture real-time energy deprivation. This study leverages smart meter data from 5,984 households in Montreal to develop a data-driven approach for detecting and alleviating energy poverty. Daily electricity usage profiles are clustered using K-Means into low-, medium-, and high-load groups. Results show that energy-poor households typically fall into the low-load cluster and exhibit significantly lower consumption, even after controlling for living area. The model enhances the detection of energy-poor households by integrating floor space data and a direct energy deprivation metric. An Increasing Block Tariff (IBT) scheme is simulated to address their limited energy access. The results indicate that IBT increases electricity use while reducing annual expenditure for energy-poor households, without increasing average prices across the system. This research contributes a novel combination of clustering and pricing policy analysis to energy poverty research and demonstrates the potential of smart meter analytics for equitable energy policy design.

Keywords

Status: accepted

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