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| Content Provider | Springer Nature Link |
|---|---|
| Author | Flath, Christoph Nicolay, David Conte, Tobias Dinther, Clemens Filipova Neumann, Lilia |
| Copyright Year | 2012 |
| Abstract | The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a cluster analysis of smart metering data focused on the customers’ time-based consumption behavior allows for a detailed customer segmentation. In the article we present a cluster analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a cluster analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the cluster analysis and highlight options to apply them to segment-specific tariff design. |
| Starting Page | 31 |
| Ending Page | 39 |
| Page Count | 9 |
| File Format | |
| ISSN | 23637005 |
| Journal | Business & Information Systems Engineering |
| Volume Number | 4 |
| Issue Number | 1 |
| e-ISSN | 18670202 |
| Language | English |
| Publisher | SP Gabler Verlag |
| Publisher Date | 2012-01-12 |
| Publisher Place | Wiesbaden |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Smart metering Energy industry Data mining Business intelligence Customer segmentation Tariff design Computing Milieux Business/Management Science Science Economics/Management Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Information Systems |
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