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.
Original languageEnglish
Title of host publicationCompanion Proceedings of the The Web Conference 2018
Place of PublicationRepublic and Canton of Geneva, Switzerland
PublisherInternational World Wide Web Conferences Steering Committee
Number of pages4
ISBN (Print)978-1-4503-5640-4
Publication statusPublished - 2018
EventWWW 2018: The Web Conference - Bridging natural and artificial intelligence worldwide - Lyon, France
Duration: 23 Apr 201827 Apr 2018

Publication series

NameWWW '18
PublisherInternational World Wide Web Conferences Steering Committee


ConferenceWWW 2018
Abbreviated titleWWW 2018
Internet address

    Research areas

  • energy consumption, machine learning, social media

ID: 45467509