Explaining and predicting annual electricity demand of enterprises – a case study from Switzerland

Faculty/Professorship: Information Systems and Energy Efficient Systems  
Author(s): Stingl, Carlo ; Hopf, Konstantin  ; Staake, Thorsten
Title of the Journal: Energy Informatics
ISSN: 2520-8942
Corporate Body: 7th D-A-CH+ Energy Informatics 2018, Oldenburg
Publisher Information: Cham : Springer International Publishing
Year of publication: 2018
Volume: 1
Issue: Suppl. 1
Pages: 50
Language(s): English
DOI: 10.1186/s42162-018-0028-0
URL: https://opus4.kobv.de/opus4-bamberg/frontdoor/i...
In an attempt to channel sales activities, companies often focus on ‘high value targets’ that offer attractive prospective returns. In liberalized electricity markets, commercial customers with high electricity demand constitute such high value targets. The problem when acquiring new customers, however, is that the electricity consumption is not known to the sales organization in advance. This hinders the possibility to prioritize sales targets and thus increases the acquisition cost, reduces the competitiveness within the market and ultimately leads to higher cost for electricity customers. In this study, we investigate the annual electricity consumption of enterprises by means of a dataset with 1810 company addresses in a typical town in Switzerland. We use the industry branch of the enterprises together with open big data (geographic information, online-content, social media data and governmental statistical data) to explain and predict the electricity consumption of such. Our linear regression analysis shows that information on the economic branches of the enterprises, basal area of buildings, number of opening hours and social media data can explain up to 19% of variance in electricity consumption. Economic trends (e.g., in labor market and turnover statistics) reflect changes in the electricity consumption in the investigated years 2010–2014 for several economic branches.

We show, that the electricity consumption can be predicted better than a random predictor, however with a high uncertainty. Nevertheless, the open data sources can be used to identify a relevant group of companies with high consumption (more than 100,000kWh per year) with good accuracy.
Keywords: Enterprise electricity consumption, Open big data, Load prediction, Machine Learning, High consumption customers
Peer Reviewed: Ja
International Distribution: Ja
Open Access Journal: Ja
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/44881
Year of publication: 10. December 2018