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

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

de Kok, Roos (Student TU Delft)
Mauri, A. (TU Delft Web Information Systems)
Bozzon, A. (TU Delft Web Information Systems)

2019

Understanding and improving the energy consumption behavior of individuals is considered a powerful approach to improve energy conservation and stimulate energy efficiency. To 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. 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 paper, we propose to use social media as a complementary source of information for understanding energy-consuming activities. A huge amount of social media posts are generated by hundreds of millions of people every day, they are publicly available, and provide real-time data often tagged to space and time. We design an ontology to get a better understanding of the energy-consuming activities domain and develop a text and image processing pipeline to extract from social media the description of energy-consuming activities. We run a case study on Istanbul and Amsterdam. We highlight the strength and weakness of our approach, showing that social media data has the potential to be a complementary source of information for describing energy-consuming activities. C 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Energy consumption
Energy-consuming activities
Machine learning
Ontology
Social media

http://resolver.tudelft.nl/uuid:7d65120e-01eb-4f80-94b2-36e1bf2da971

https://doi.org/10.3390/en12010015

1996-1073

Energies, 12 (1), 1-28

Institutional Repository

journal article

© 2019 Roos de Kok, A. Mauri, A. Bozzon