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Predicting Energy Customer Vulnerability Using Smart Meter Data

Research output: Working paper

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Publication date2018
Number of pages22
<mark>Original language</mark>English

Abstract

Supporting vulnerable consumers and reducing fuel poverty are major priorities for policy makers in the energy sector. With the availability of streaming
data from smart meters we are able to develop simple and reliable methods of
identifying vulnerable energy customers and as a result develop targeted policy
interventions. This study investigates how vulnerable customers can be identified from natural gas consumption data. Neural networks, random forest, naive
Bayes, and support vector machines were assessed for classification of consumer
vulnerability. Random forest, with the prediction accuracy of 94.6 percent, outperforms other prediction models. Our study provides additional evidence that
machine learning methods can be deployed by policymakers and insights teams
to predict vulnerability from patterns of consumer behaviour.