Machine Learning for Green Smart Homes

Brian O’Regan; Fábio Silva; Paula Carroll; Xavier Dubuisson; Pádraig Lyons

doi:10.1007/978-3-030-96429-0_2

Machine Learning for Green Smart Homes

Brian O’Regan
, Fábio Silva
, Paula Carroll
, Xavier Dubuisson
, Pádraig Lyons

Tyndall IPES (IERC)

University College Dublin
XD Sustainable Energy Consulting Ltd

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

Abstract

Smarter approaches to data processing are essential to realise the potential benefits of the exponential growth in energy data in homes from a variety of sources, such as smart metres, sensors and other devices. Machine learning encompasses several techniques to process and visualise data. Each technique is specifically suited to certain data types and problems, whether it be supervised, unsupervised or reinforcement learning. These techniques can be applied to increase the efficient use of energy within a home, enable better and more accurate home owner decision-making and help contribute to greener building stock. This chapter presents the state of the art in this area and looks forward to potential new uses for machine learning in renewable energy data.

Original language	English
Title of host publication	Green Energy and Technology
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	41-66
Number of pages	26
DOIs	https://doi.org/10.1007/978-3-030-96429-0_2
Publication status	Published - 2022

Publication series

Name	Green Energy and Technology
ISSN (Print)	1865-3529
ISSN (Electronic)	1865-3537

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 12 Responsible Consumption and Production

Keywords

Big data
Energy management
Energy modelling
Green buildings
Machine learning
Smart home

Access to Document

10.1007/978-3-030-96429-0_2

Cite this

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title = "Machine Learning for Green Smart Homes",

abstract = "Smarter approaches to data processing are essential to realise the potential benefits of the exponential growth in energy data in homes from a variety of sources, such as smart metres, sensors and other devices. Machine learning encompasses several techniques to process and visualise data. Each technique is specifically suited to certain data types and problems, whether it be supervised, unsupervised or reinforcement learning. These techniques can be applied to increase the efficient use of energy within a home, enable better and more accurate home owner decision-making and help contribute to greener building stock. This chapter presents the state of the art in this area and looks forward to potential new uses for machine learning in renewable energy data.",

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TY - CHAP

T1 - Machine Learning for Green Smart Homes

AU - O’Regan, Brian

AU - Silva, Fábio

AU - Carroll, Paula

AU - Dubuisson, Xavier

AU - Lyons, Pádraig

PY - 2022

Y1 - 2022

N2 - Smarter approaches to data processing are essential to realise the potential benefits of the exponential growth in energy data in homes from a variety of sources, such as smart metres, sensors and other devices. Machine learning encompasses several techniques to process and visualise data. Each technique is specifically suited to certain data types and problems, whether it be supervised, unsupervised or reinforcement learning. These techniques can be applied to increase the efficient use of energy within a home, enable better and more accurate home owner decision-making and help contribute to greener building stock. This chapter presents the state of the art in this area and looks forward to potential new uses for machine learning in renewable energy data.

AB - Smarter approaches to data processing are essential to realise the potential benefits of the exponential growth in energy data in homes from a variety of sources, such as smart metres, sensors and other devices. Machine learning encompasses several techniques to process and visualise data. Each technique is specifically suited to certain data types and problems, whether it be supervised, unsupervised or reinforcement learning. These techniques can be applied to increase the efficient use of energy within a home, enable better and more accurate home owner decision-making and help contribute to greener building stock. This chapter presents the state of the art in this area and looks forward to potential new uses for machine learning in renewable energy data.

KW - Big data

KW - Energy management

KW - Energy modelling

KW - Green buildings

KW - Machine learning

KW - Smart home

UR - https://www.scopus.com/pages/publications/85128956374

U2 - 10.1007/978-3-030-96429-0_2

DO - 10.1007/978-3-030-96429-0_2

M3 - Chapter

AN - SCOPUS:85128956374

T3 - Green Energy and Technology

SP - 41

EP - 66

BT - Green Energy and Technology

PB - Springer Science and Business Media Deutschland GmbH

ER -

Machine Learning for Green Smart Homes

Abstract

Publication series

UN SDGs

Keywords

Access to Document

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