Self-organizing maps
Data mining: concepts and techniques
Data mining: concepts and techniques
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Visual Explorations in Finance
Visual Explorations in Finance
Self-Organizing Maps
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Neural Networks for Applied Sciences and Engineering
Neural Networks for Applied Sciences and Engineering
CRM Segmentation and Clustering Using SAS Enterprise Miner
CRM Segmentation and Clustering Using SAS Enterprise Miner
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The ongoing deployment of Automated Meter Reading systems (AMR) in the European electricity industry has created new challenges for electricity utilities in terms of how to fully utilise the wealth of timely measured AMR data, not only to enhance day-to-day operations, but also to facilitate demand response programs. In this study we investigate a visual data mining approach for decision-making support with respect to pricing differentiation or designing demand response tariffs. We cluster the customers in our sample according to the customers' actual consumption behaviour in 2009, and profile their electricity consumption with a focus on the comparison of two sets of seasonal and time based variables. The results suggest that such an analytical approach can visualise deviations and granular information in consumption patterns, allowing the electricity companies to gain better knowledge about the customers' electricity usage. The investigated electricity consumption time series profiling approach will add empirical understanding of the problem domain to the related research community and to the future practice of the energy industry.