Abstract
BackgroundUsers 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.MethodsThis 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).ResultsThe 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.ConclusionThe 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.
Highlights
Users create serious amounts of Volunteered Geographic Information (VGI) in Online platforms like OpenStreetMap or in real estate portals
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
This paper aims to motivate the use of VGI, illustrates the usage of such data and the possible contribution for predictive data analytics in the field of energy retail considering two prominent examples of VGI initiatives
Summary
Users create serious amounts of Volunteered Geographic Information (VGI) in Online platforms like OpenStreetMap or in real estate portals. Predictive data analytics becomes increasingly important for organizations and enable them to remain competitive and agile in continuously changing markets (Constantiou and Kallinikos 2015; Gillon et al 2012; Mithas et al 2013; Sharma et al 2014) This holds especially for energy utilities that are exposed to a groundbreaking market transition, affecting them from the production and sales perspective. Big data analytics in the energy industry Utility companies hold, millions of data points on their customers that can be developed to a valuable business asset. This available data will further increase in the future due to the roll-out of smart meter and Internet of things infrastructures. This work contributes to this research gap and investigates the use of freely available geographic data for predictive energy data analytics in the specific but relevant example of household classification
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