Ph.D. Forum: Intelligent Home Energy Management: Developing AI-Driven Systems for Sustainable Living

Yu Sheng

doi:10.1145/3666025.3699665

Ph.D. Forum: Intelligent Home Energy Management: Developing AI-Driven Systems for Sustainable Living

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Abstract

The MAI-HOME project, funded by the Interreg initiative, addresses energy poverty and CO2 emission reduction through an AI-driven framework tailored for vulnerable populations. This research spans three years of data collection from multiple sensors installed in every room of sixteen houses across the Netherlands and Belgium. It aims to predict and promote energy-saving behaviors effectively. Utilizing an innovative blend of digital twins and robust data privacy measures, this project explores four critical areas: real-time data collection, predictive AI model development, data privacy enhancement, and behavioral intervention strategies. Initial findings suggest promising avenues for technological advancements and societal benefits in sustainable energy practices.

Original language	English
Title of host publication	SenSys 2024
Subtitle of host publication	Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems
Publisher	Association for Computing Machinery, Inc
Pages	914-915
Number of pages	2
ISBN (Electronic)	9798400706974
DOIs	https://doi.org/10.1145/3666025.3699665
Publication status	Published - 4 Nov 2024
Event	22nd ACM Conference on Embedded Networked Sensor Systems - Hangzhou, China Duration: 4 Nov 2024 → 7 Nov 2024 Conference number: 22 https://sensys.acm.org/2024/

Conference

Conference	22nd ACM Conference on Embedded Networked Sensor Systems
Abbreviated title	SenSys 2024
Country/Territory	China
City	Hangzhou
Period	4/11/24 → 7/11/24
Internet address	https://sensys.acm.org/2024/

Keywords

energy management
machine learning
non-intrusive sensors
occupancy inference
PIR sensors
transfer learning
transformers

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10.1145/3666025.3699665Licence: CC BY

Cite this

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title = "Ph.D. Forum: Intelligent Home Energy Management: Developing AI-Driven Systems for Sustainable Living",

abstract = "The MAI-HOME project, funded by the Interreg initiative, addresses energy poverty and CO2 emission reduction through an AI-driven framework tailored for vulnerable populations. This research spans three years of data collection from multiple sensors installed in every room of sixteen houses across the Netherlands and Belgium. It aims to predict and promote energy-saving behaviors effectively. Utilizing an innovative blend of digital twins and robust data privacy measures, this project explores four critical areas: real-time data collection, predictive AI model development, data privacy enhancement, and behavioral intervention strategies. Initial findings suggest promising avenues for technological advancements and societal benefits in sustainable energy practices.",

keywords = "energy management, machine learning, non-intrusive sensors, occupancy inference, PIR sensors, transfer learning, transformers",

author = "Yu Sheng",

note = "Publisher Copyright: {\textcopyright} 2024 Copyright is held by the owner/author(s).; 22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024 ; Conference date: 04-11-2024 Through 07-11-2024",

year = "2024",

month = nov,

day = "4",

doi = "10.1145/3666025.3699665",

language = "English",

pages = "914--915",

booktitle = "SenSys 2024",

publisher = "Association for Computing Machinery, Inc",

address = "United States",

url = "https://sensys.acm.org/2024/",

}

Sheng, Y 2024, Ph.D. Forum: Intelligent Home Energy Management: Developing AI-Driven Systems for Sustainable Living. in SenSys 2024: Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems. Association for Computing Machinery, Inc, pp. 914-915, 22nd ACM Conference on Embedded Networked Sensor Systems, Hangzhou, China, 4/11/24. https://doi.org/10.1145/3666025.3699665

Ph.D. Forum: Intelligent Home Energy Management: Developing AI-Driven Systems for Sustainable Living. / Sheng, Yu.
SenSys 2024: Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems. Association for Computing Machinery, Inc, 2024. p. 914-915.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

TY - GEN

T1 - Ph.D. Forum: Intelligent Home Energy Management

T2 - 22nd ACM Conference on Embedded Networked Sensor Systems

AU - Sheng, Yu

N1 - Conference code: 22

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Y1 - 2024/11/4

N2 - The MAI-HOME project, funded by the Interreg initiative, addresses energy poverty and CO2 emission reduction through an AI-driven framework tailored for vulnerable populations. This research spans three years of data collection from multiple sensors installed in every room of sixteen houses across the Netherlands and Belgium. It aims to predict and promote energy-saving behaviors effectively. Utilizing an innovative blend of digital twins and robust data privacy measures, this project explores four critical areas: real-time data collection, predictive AI model development, data privacy enhancement, and behavioral intervention strategies. Initial findings suggest promising avenues for technological advancements and societal benefits in sustainable energy practices.

AB - The MAI-HOME project, funded by the Interreg initiative, addresses energy poverty and CO2 emission reduction through an AI-driven framework tailored for vulnerable populations. This research spans three years of data collection from multiple sensors installed in every room of sixteen houses across the Netherlands and Belgium. It aims to predict and promote energy-saving behaviors effectively. Utilizing an innovative blend of digital twins and robust data privacy measures, this project explores four critical areas: real-time data collection, predictive AI model development, data privacy enhancement, and behavioral intervention strategies. Initial findings suggest promising avenues for technological advancements and societal benefits in sustainable energy practices.

KW - energy management

KW - machine learning

KW - non-intrusive sensors

KW - occupancy inference

KW - PIR sensors

KW - transfer learning

KW - transformers

U2 - 10.1145/3666025.3699665

DO - 10.1145/3666025.3699665

M3 - Conference Article in proceeding

AN - SCOPUS:85211807581

SP - 914

EP - 915

BT - SenSys 2024

PB - Association for Computing Machinery, Inc

Y2 - 4 November 2024 through 7 November 2024

ER -

Ph.D. Forum: Intelligent Home Energy Management: Developing AI-Driven Systems for Sustainable Living

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Keywords

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