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Social Smart Meter: Identifying Energy Consumption Behavior in User-Generated Content. / Mauri, Andrea; Psyllidis, Achilleas; Bozzon, Alessandro.

Companion Proceedings of the The Web Conference 2018. Republic and Canton of Geneva, Switzerland : International World Wide Web Conferences Steering Committee, 2018. p. 195-198 (WWW '18).

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Mauri, A, Psyllidis, A & Bozzon, A 2018, Social Smart Meter: Identifying Energy Consumption Behavior in User-Generated Content. in Companion Proceedings of the The Web Conference 2018. WWW '18, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 195-198, WWW 2018, Lyon, France, 23/04/18. https://doi.org/10.1145/3184558.3186977

APA

Mauri, A., Psyllidis, A., & Bozzon, A. (2018). Social Smart Meter: Identifying Energy Consumption Behavior in User-Generated Content. In Companion Proceedings of the The Web Conference 2018 (pp. 195-198). (WWW '18). Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/3184558.3186977

Vancouver

Mauri A, Psyllidis A, Bozzon A. Social Smart Meter: Identifying Energy Consumption Behavior in User-Generated Content. In Companion Proceedings of the The Web Conference 2018. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. 2018. p. 195-198. (WWW '18). https://doi.org/10.1145/3184558.3186977

Author

Mauri, Andrea ; Psyllidis, Achilleas ; Bozzon, Alessandro. / Social Smart Meter: Identifying Energy Consumption Behavior in User-Generated Content. Companion Proceedings of the The Web Conference 2018. Republic and Canton of Geneva, Switzerland : International World Wide Web Conferences Steering Committee, 2018. pp. 195-198 (WWW '18).

BibTeX

@inproceedings{2f975d6ca1f14e59a5b9bb23a47fb7aa,
title = "Social Smart Meter: Identifying Energy Consumption Behavior in User-Generated Content",
abstract = "Having a thorough understanding of energy consumption behavior is an important element of sustainability studies. Traditional sources of information about energy consumption, such as smart meter devices and surveys, can be costly to deploy, may lack contextual information or have infrequent updates. In this paper, we examine the possibility of extracting energy consumption-related information from user-generated content. More specifically, we develop a pipeline that helps identify energy-related content in Twitter posts and classify it into four categories (dwelling, food, leisure, and mobility), according to the type of activity performed. We further demonstrate a web-based application--called Social Smart Meter--that implements the proposed pipeline and enables different stakeholders to gain an insight into daily energy consumption behavior, as well as showcase it in case studies involving several world cities.",
keywords = "energy consumption, machine learning, social media",
author = "Andrea Mauri and Achilleas Psyllidis and Alessandro Bozzon",
note = "Accepted Author Manuscript",
year = "2018",
doi = "10.1145/3184558.3186977",
language = "English",
isbn = "978-1-4503-5640-4",
series = "WWW '18",
publisher = "International World Wide Web Conferences Steering Committee",
pages = "195--198",
booktitle = "Companion Proceedings of the The Web Conference 2018",
address = "Switzerland",

}

RIS

TY - GEN

T1 - Social Smart Meter: Identifying Energy Consumption Behavior in User-Generated Content

AU - Mauri, Andrea

AU - Psyllidis, Achilleas

AU - Bozzon, Alessandro

N1 - Accepted Author Manuscript

PY - 2018

Y1 - 2018

N2 - Having a thorough understanding of energy consumption behavior is an important element of sustainability studies. Traditional sources of information about energy consumption, such as smart meter devices and surveys, can be costly to deploy, may lack contextual information or have infrequent updates. In this paper, we examine the possibility of extracting energy consumption-related information from user-generated content. More specifically, we develop a pipeline that helps identify energy-related content in Twitter posts and classify it into four categories (dwelling, food, leisure, and mobility), according to the type of activity performed. We further demonstrate a web-based application--called Social Smart Meter--that implements the proposed pipeline and enables different stakeholders to gain an insight into daily energy consumption behavior, as well as showcase it in case studies involving several world cities.

AB - Having a thorough understanding of energy consumption behavior is an important element of sustainability studies. Traditional sources of information about energy consumption, such as smart meter devices and surveys, can be costly to deploy, may lack contextual information or have infrequent updates. In this paper, we examine the possibility of extracting energy consumption-related information from user-generated content. More specifically, we develop a pipeline that helps identify energy-related content in Twitter posts and classify it into four categories (dwelling, food, leisure, and mobility), according to the type of activity performed. We further demonstrate a web-based application--called Social Smart Meter--that implements the proposed pipeline and enables different stakeholders to gain an insight into daily energy consumption behavior, as well as showcase it in case studies involving several world cities.

KW - energy consumption, machine learning, social media

U2 - 10.1145/3184558.3186977

DO - 10.1145/3184558.3186977

M3 - Conference contribution

SN - 978-1-4503-5640-4

T3 - WWW '18

SP - 195

EP - 198

BT - Companion Proceedings of the The Web Conference 2018

PB - International World Wide Web Conferences Steering Committee

CY - Republic and Canton of Geneva, Switzerland

ER -

ID: 45467509