Name
Title | ||
|---|---|---|
Behavior pattern recognition in electric power consumption series using data mining tools |
Abstract | ||
|---|---|---|
The behavioral patterns identification is very important for time series analysis of energy consumption to assist planning activities and decision making, as well to seek improvements in service quality and financial benefits. In this paper we used a methodology based on data mining tools, including cluster analysis and time series representation. The Time Series Knowledge Mining [1] was adapted to the treatment of consumption electricity series. Results are shown in a case study with hourly consumption measurements of eight power substations. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-32639-4_64 | IDEAL |
Keywords | Field | DocType |
energy consumption,time series analysis,electric power consumption series,behavior pattern recognition,hourly consumption measurement,behavioral patterns identification,data mining tool,case study,time series representation,consumption electricity series,time series knowledge mining,cluster analysis,time series | Knowledge mining,Electric power,Time series,Behavioral pattern,Data mining,Time series representation,Service quality,Electricity,Computer science,Energy consumption | Conference |
Citations | PageRank | References |
1 | 0.40 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alynne C. S. de Queiroz | 1 | 1 | 0.40 |
| José Alfredo F. Costa | 2 | 52 | 10.11 |