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Our world population is increasing significantly resulting in a growing energy demand while the earth is running out if natural resources. This expanded energy demand directly affects climate change. Hence, there is an urgent need to move towards urban sustainability and to reduce our energy consumption. This calls upon a behavioral change in energy consumption by the individuals (i.e., citizens). Social comparison, in the form of comparative feedback on energy usage with others, appears to be a more effective approach to stimulate energy conservation and efficiency than temporal self-comparisons. But before we can motivate people to change their energy consumption behavior, we need to have a thorough understanding of which energy-consuming activities they perform and how these are performed. Thus, insights into the individual’s activities related to energy consumption should be gathered at a high-granular level. Traditional sources of information about energy consumption, such as smart sensor devices and surveys, can be costly to set-up, may lack contextual information, have infrequent updates or are not publicly accessible. In this research, we propose to use user-generated content - and specifically, social media content - as a complementary source of information due to its rich and semantic nature. A huge amount of social media data is generated by hundreds of millions of people every day. These data sources are also publicly available and provide real-time data which is often tagged to space and time. Social media data also contains a lot of meta data, making it a good source for the recognition of energy-consuming activities performed by individuals. This thesis contributes the Social Smart Meter framework in order to meet the aim of automatically processing user-generated content for the description of energy-consuming activities, both at individual and group level. Four different categories of energy-consuming activities are distinguished: dwelling, food consumption, leisure, and mobility. To get a better understanding of the domain of energy-consuming activities, we contribute the Social Smart Meter Ontology (SSMO). This ontology forms the base for the data processing pipeline, which is developed in order to collect and enrich the data using several state-of-the-art techniques. Hereafter, the enriched data is classified to the different categories of energy-consuming activities using a dictionary- and rule-based approach, along with a classification confidence. To find ground truth and to evaluate the framework’s performance, a user-based evaluation approach was used. Furthermore, we contribute a Web-based application to support the analyses at group (i.e., city and neighborhood) level. Case studies are performed for the cities of Amsterdam and Istanbul, for which 275K social media posts are collected. The aggregated results are analyzed, providing more insights into the energy-consuming activities identified in the collected social media content. The majority of the classified social media posts refers to leisure activities. In addition, by examining for each post whether there exists a (significant) distance to the previous post created by this user, many mobility activities are inferred. The case studies also contribute to the evaluation and discussion of the framework’s performance; by analyzing its results, the framework’s adherence to reality was discussed. Based on our preliminary results, it seems that using user-generated content has great potential as a complementary source of information for identifying and describing energy-consuming activities that are not yet captured by traditional data sources.

Automatic Processing of User-Generated Content for the Description of Energy-Consuming Activities at Individual and Group Level