BASIN CLUSTERING OF TURKEY BY USE OF MONTHLY STREAM-FLOW DATA

被引：1

作者：

Arslan, Yusuf ^{[1
]}

Birturk, Aysenur ^{[2
]}

Eren, Sinan ^{[1
]}

机构：

[1] TUBITAK MAM Energy Inst, Ankara, Turkey

[2] Middle East Tech Univ, Ankara, Turkey

来源：

2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) | 2015年

关键词：

stream-flow rate; longest common subsequence; k-means; dynamic time warping; hierarchical clustering; basin based clustering;

D O I：

10.1109/ICMLA.2015.82

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Security of the energy supply is an important topic in energy field. It has two parts which are supply and demand. To ensure that demand is met, the supply at the specific time points has to be known or predicted. Supply is predicted by use of seasonal, yearly and regional information. The streamflow dataset resolution is monthly and it supplies the yearly and seasonal information. The only missing part for supply prediction is the regional information. The aim of this study to find the basin based regional clustering of the streams and correspondingly hydroelectric power plants. In this paper, 14 out of 26 basins of Turkey, which contain over 80% of the hydroelectric power plants of Turkey in Dispatcher Information System, are clustered by use of different clustering techniques. Results are visualized on Turkey basin map.

引用

页码：1169 / 1174

页数：6

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