A Short Review on Data Mining Techniques for Electricity Customers Characterization

被引：0

作者：

Cembranel, Samuel S. ^{[1
,2
]}

Lezama, Fernando ^{[1
]}

Soares, Joao ^{[1
]}

Ramos, Sergio ^{[1
]}

Gomes, Antonio ^{[1
]}

Vale, Zita ^{[3
]}

机构：

[1] Polytech Porto, GECAD, Porto, Portugal

[2] IFSC, Florianopolis, SC, Brazil

[3] Polytech Porto, Porto, Portugal

来源：

2019 IEEE PES GTD GRAND INTERNATIONAL CONFERENCE AND EXPOSITION ASIA (GTD ASIA) | 2019年

关键词：

Classification; Clustering; Data Mining; Knowledge Discovery in Databases; Load Profiling; LOAD PROFILES; PATTERN-RECOGNITION; CLASSIFICATION; ALGORITHMS; METHODOLOGY; FRAMEWORK; MODEL;

D O I：

10.1109/gtdasia.2019.8715891

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页码：194 / 199

页数：6

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