Loading...

Media is loading
 

Paper Type

ERF

Abstract

Energy poverty occurs when households do not have access to, or cannot afford the energy services necessary to support their daily needs, including heating/cooling, washing, cooking, lighting and other activities. Energy poverty is a hidden, but important social problem because living in conditions without adequate energy access can lead to other health and social problems. Addressing energy poverty in a meaningful way requires first detecting households in precarious situations and then putting in place appropriate strategies to support them. In this research, we explore the potential for using artificial intelligence (AI) to model energy patterns that reflect situations of poverty. Using simulated energy consumption data in the New Zealand context, we show that AI, specifically machine learning models can achieve high predictive accuracy. We discuss challenges associated with using finer-grained approaches and opportunities for better prediction, prevention, and remediation of energy poverty.

Paper Number

1718

Author Connect URL

https://authorconnect.aisnet.org/conferences/AMCIS2024/papers/1718

Comments

SIGGREEN

Author Connect Link

Share

COinS
Best Paper Nominee badge
Top 25 Paper Badge
 
Aug 16th, 12:00 AM

Tackling Energy Poverty with Artificial Intelligence: Challenges and Opportunities

Energy poverty occurs when households do not have access to, or cannot afford the energy services necessary to support their daily needs, including heating/cooling, washing, cooking, lighting and other activities. Energy poverty is a hidden, but important social problem because living in conditions without adequate energy access can lead to other health and social problems. Addressing energy poverty in a meaningful way requires first detecting households in precarious situations and then putting in place appropriate strategies to support them. In this research, we explore the potential for using artificial intelligence (AI) to model energy patterns that reflect situations of poverty. Using simulated energy consumption data in the New Zealand context, we show that AI, specifically machine learning models can achieve high predictive accuracy. We discuss challenges associated with using finer-grained approaches and opportunities for better prediction, prevention, and remediation of energy poverty.

When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.