Revealing household characteristics from smart meter data

Christian Beckel, Leyna Sadamori, Thorsten Staake and Silvia Santini

Energy, 2014, vol. 78, issue C, 397-410

Abstract: Utilities are currently deploying smart electricity meters in millions of households worldwide to collect fine-grained electricity consumption data. We present an approach to automatically analyzing this data to enable personalized and scalable energy efficiency programs for private households. In particular, we develop and evaluate a system that uses supervised machine learning techniques to automatically estimate specific “characteristics” of a household from its electricity consumption. The characteristics are related to a household's socio-economic status, its dwelling, or its appliance stock. We evaluate our approach by analyzing smart meter data collected from 4232 households in Ireland at a 30-min granularity over a period of 1.5 years. Our analysis shows that revealing characteristics from smart meter data is feasible, as our method achieves an accuracy of more than 70% over all households for many of the characteristics and even exceeds 80% for some of the characteristics. The findings are applicable to all smart metering systems without making changes to the measurement infrastructure. The inferred knowledge paves the way for targeted energy efficiency programs and other services that benefit from improved customer insights. On the basis of these promising results, the paper discusses the potential for utilities as well as policy and privacy implications.

Keywords: Data-driven energy efficiency; Domestic electricity consumption; Electricity load profiles; Automated customer segmentation; Supervised machine learning (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (54)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:78:y:2014:i:c:p:397-410

DOI: 10.1016/j.energy.2014.10.025

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