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Generating Insights from Smart Meter Data: Challenges and Opportunities

Research output: ThesisDoctoral Thesis

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Generating Insights from Smart Meter Data : Challenges and Opportunities. / Ushakova, Anastasia.

University College London, 2019.

Research output: ThesisDoctoral Thesis

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@phdthesis{d9bb52cf374a44fd9d4a04f5c89e796e,
title = "Generating Insights from Smart Meter Data: Challenges and Opportunities",
abstract = "The introduction of smart meter technology has been central to recent innovations in energy provision for the UK residential sector. Smart meters have the potential to give greater insight into energy consumption behaviour for energy providers and researchers alike. For example, they may aid our understanding of how the consumption of gas and electricity may be replaced by the energy from renewable sources, or how consumer behaviours can be changed to reduce overall energy consumption, increase efficiency, and lessen the pressure on the national grid networks. The advantage of a thorough understanding of the insights generated from smart meter data for policy issues may sound obvious at a first glance. However, there are significant challenges associated with the availability of methods and computation necessary to perform a complete analysis of the available data. The thesis provides an in depth look at the nature of energy consumption through an analysis of big data that is recorded by around 400,000 smart meters installed at residential properties across the UK. It further discusses how this data is different from perhaps more conventionally collected retail consumer data, and in what way does the temporal nature of these data reveal information about the customers dynamics without compromising their anonymity. Various machine learning methods are applied and surveyed against more conventional methods often used by researchers and industry practitioners. Some extensions to improve the accuracy and reliability of methods for both segmentation of the behaviour, and prediction are also suggested. Lastly, a case study looking at identifying the fuel poor from smart meter data is presented as an illustrative example of potential research questions one may answer with smart meter data records.",
keywords = "smart meter data, big data, classification",
author = "Anastasia Ushakova",
year = "2019",
language = "English",
publisher = "University College London",
school = "University College London",

}

RIS

TY - THES

T1 - Generating Insights from Smart Meter Data

T2 - Challenges and Opportunities

AU - Ushakova, Anastasia

PY - 2019

Y1 - 2019

N2 - The introduction of smart meter technology has been central to recent innovations in energy provision for the UK residential sector. Smart meters have the potential to give greater insight into energy consumption behaviour for energy providers and researchers alike. For example, they may aid our understanding of how the consumption of gas and electricity may be replaced by the energy from renewable sources, or how consumer behaviours can be changed to reduce overall energy consumption, increase efficiency, and lessen the pressure on the national grid networks. The advantage of a thorough understanding of the insights generated from smart meter data for policy issues may sound obvious at a first glance. However, there are significant challenges associated with the availability of methods and computation necessary to perform a complete analysis of the available data. The thesis provides an in depth look at the nature of energy consumption through an analysis of big data that is recorded by around 400,000 smart meters installed at residential properties across the UK. It further discusses how this data is different from perhaps more conventionally collected retail consumer data, and in what way does the temporal nature of these data reveal information about the customers dynamics without compromising their anonymity. Various machine learning methods are applied and surveyed against more conventional methods often used by researchers and industry practitioners. Some extensions to improve the accuracy and reliability of methods for both segmentation of the behaviour, and prediction are also suggested. Lastly, a case study looking at identifying the fuel poor from smart meter data is presented as an illustrative example of potential research questions one may answer with smart meter data records.

AB - The introduction of smart meter technology has been central to recent innovations in energy provision for the UK residential sector. Smart meters have the potential to give greater insight into energy consumption behaviour for energy providers and researchers alike. For example, they may aid our understanding of how the consumption of gas and electricity may be replaced by the energy from renewable sources, or how consumer behaviours can be changed to reduce overall energy consumption, increase efficiency, and lessen the pressure on the national grid networks. The advantage of a thorough understanding of the insights generated from smart meter data for policy issues may sound obvious at a first glance. However, there are significant challenges associated with the availability of methods and computation necessary to perform a complete analysis of the available data. The thesis provides an in depth look at the nature of energy consumption through an analysis of big data that is recorded by around 400,000 smart meters installed at residential properties across the UK. It further discusses how this data is different from perhaps more conventionally collected retail consumer data, and in what way does the temporal nature of these data reveal information about the customers dynamics without compromising their anonymity. Various machine learning methods are applied and surveyed against more conventional methods often used by researchers and industry practitioners. Some extensions to improve the accuracy and reliability of methods for both segmentation of the behaviour, and prediction are also suggested. Lastly, a case study looking at identifying the fuel poor from smart meter data is presented as an illustrative example of potential research questions one may answer with smart meter data records.

KW - smart meter data

KW - big data

KW - classification

M3 - Doctoral Thesis

PB - University College London

ER -