| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 1.87 MB | Adobe PDF |
Advisor(s)
Abstract(s)
An important tool to manage electrical systems is the knowledge of customers' consumption patterns. Data Mining (DM) emerges as an important tool for extracting information about energy consumption in databases and identifying consumption patterns. This paper presents a short review on DM, with a focus on the characterization of electricity customers supported on knowledge discovery in database (KDD) process. The study includes several steps: first, few concepts of the KDD process are presented; following, a short review of clustering algorithms is presented including partitional, hierarchical, fuzzy, evolutionary methods, and Self-Organizing Maps; finally, the main concepts and methods for load classification, based on load shape indices are presented. The main objective of this work is to present a short review of DM techniques applied to identify typical load profiles in electrical systems and new customers' classification.
Description
Keywords
Classification Clustering Data Mining Knowledge Discovery in Databases Load Profiling
Citation
Publisher
Institute of Electrical and Electronics Engineers