Mining volunteered geographic information for predictive energy data analytics

• Main Author: Konstantin Hopf
• Format: Article
• Language: English
• Published: SpringerOpen 2018-07-01
• Series: Energy Informatics
• Subjects: Volunteered geographic information; Energy data analytics; Energy retail; Predictive analytics; Machine learning; Household classification
• Online Access: http://link.springer.com/article/10.1186/s42162-018-0009-3

Abstract Background Users create serious amounts of Volunteered Geographic Information (VGI) in Online platforms like OpenStreetMap or in real estate portals. Harvesting such data with the help of business analytics and machine learning methods yield promising opportunities for firms to create additional business value through mining their internal and external data sources. Energy retailers can benefit from these achievements in particular, because they need to establish richer customer relations, but their customer insights are currently limited. Extending this knowledge, these established companies can develop customer-specific offerings and promote them effectively. Methods This paper gives an overview to VGI data sources and presents first results from a comprehensive review of these crowd-sourced data pools. Besides that, the value of two exemplary VGI data sources (OpenStreetMap and real estate portals) for predictive analytics in energy retail is investigated by using them in a household classification algorithm that recognizes specific household characteristics (e.g., living alone, having large dwellings or electric heating). Results The empirical study with data from 3,905 household electricity customers located in Switzerland shows that VGI data can support the recognition of the 13 considered household classes significantly, and that such details can be retrieved based on VGI data alone. Conclusion The results demonstrate that the classification of customers in relevant classes is possible based on data that is present to the companies and that VGI data can help to improve the quality of predictive algorithms in the energy sector.