Back-filling Missing Data When Predicting Domestic Electricity Consumption From Smart Meter Data

TL;DR

This paper presents a method to accurately estimate annual electricity consumption from smart meter data with up to six months missing, classifies user profiles, and demonstrates economic benefits of Time-of-Use tariffs.

Contribution

It introduces a novel back-filling technique for missing smart meter data and identifies distinct consumption profiles for better tariff recommendations.

Findings

Effective back-filling of up to six months missing data.

Identification of five distinct user consumption profiles.

Time-of-Use tariffs offer economic advantages for most users.

Abstract

This study uses data from domestic electricity smart meters to estimate annual electricity bills for a whole year. We develop a method for back-filling data smart meter for up to six missing months for users who have less than one year of smart meter data, ensuring reliable estimates of annual consumption. We identify five distinct electricity consumption user profiles for homes based on day, night, and peak usage patterns, highlighting the economic advantages of Time-of-Use (ToU) tariffs over fixed tariffs for most users, especially those with higher nighttime consumption. Ultimately, the results of this study empowers consumers to manage their energy use effectively and to make informed choices regarding electricity tariff plans.