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

Abstract

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 100,000 kWh per year) with good accuracy.

Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities

“…In this paper, instead of developing pure forecasting models for each company of the community, we propose a method for generating representative electricity consumption profiles for each member, which is solely based on their past consumption data. The method is inspired by [40] and adapted to the present context.…”

Churn rate prediction in Telecommunication systems

“…Carlo Stingl et al, 2018 [10] proposed industry part of the endeavors together with open big abstracts (geographic data, online-content, amusing media abstracts and authoritative statistical data) to analyze and ahead the electricity appliance of such. Our beeline corruption analysis demonstrates that advice on the budgetary branches of the enterprises, basal area of buildings, amount of aperture hours and amusing media abstracts can acknowledge up to 19% of about-face in electricity consumption.…”