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.
Highlights
The performance of day-to-day human activities requires considerable amounts of different forms of energy
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
The proliferation of social data has given rise to a new source of information about peoples’ daily spatiotemporal behavior [2, 4, 6]. While other sources, such as mobile phones, satellite imagery, and traffic sensors are increasingly used in deriving information about energy consumption behavior, in this work we focus on user-generated content from social media
Summary
The performance of day-to-day human activities requires considerable amounts of different forms of energy. The proliferation of social data has given rise to a new source of information about peoples’ daily spatiotemporal behavior [2, 4, 6] While other sources, such as mobile phones, satellite imagery, and traffic sensors are increasingly used in deriving information about energy consumption behavior, in this work we focus on user-generated content from social media. Data from these sources are becoming widely available, inexpensive to collect, dynamic and frequently updated Given that they are the byproduct of – or refer to – daily human activities, it is reasonable to assume that information about energy consumption could be embedded in their semantic signatures. In this demo, we introduce Social Smart Meter, a web-based application that offers different users the opportunity to gain an insight into four types of energy consumption behavior (dwelling, food, leisure, and mobility), calculated at the neighborhood level. The demo showcases the extent to which data from social media could be used as a complementary source of information about individual energy consumption behavior in several world cities such as Amsterdam, Jakarta, and Boston
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