A novel methodology based on clustering techniques for automatic processing of MV feeder daily load patterns

2002 ◽  
Author(s):  
R. Lamedica ◽  
L. Santolamazza ◽  
G. Fracassi ◽  
G. Martinelli ◽  
A. Prudenzi
Keyword(s):  
Automatic Processing ◽  
Clustering Techniques ◽  
Daily Load

scholarly journals Advanced SOM & K Mean Method for Load Curve Clustering

2018 ◽  
Vol 8 (6) ◽  
pp. 4829
Author(s):  
Phan Thi Thanh Binh ◽  
Trong Nghia Le ◽  
Nui Pham Xuan
Keyword(s):  
Electricity Consumption ◽  
Subtractive Clustering ◽  
Training Algorithms ◽  
Load Curve ◽  
Data Set ◽  
Clustering Techniques ◽  
Seasonal Factors ◽  
Curve Clustering ◽  
Clustering Approach ◽  
Daily Load

From the load curve classification for one customer, the main features such as the seasonal factors, the weekday factors influencing on the electricity consumption may be extracted. By this way some utilities can make decision on the tariff by seasons or by day in week. The popular clustering techniques are the SOM & K-mean or Fuzzy K-mean. SOM &Kmean is a prominent approach for clustering with a two-level approach: first, the data set will be clustered using the SOM and in the second level, the SOM will be clustered by K-mean. In the first level, two training algorithms were examined: sequential and batch training. For the second level, the K-mean has the results that are strongly depended on the initial values of the centers. To overcome this, this paper used the subtractive clustering approach proposed by Chiu in 1994 to determine the centers. Because the effective radius in Chiu’s method has some influence on the number of centers, the paper applied the PSO technique to find the optimum radius. To valid the proposed approach, the test on well-known data samples is carried out. The applications for daily load curves of one Southern utility are presented.

Improved Statistical Methods for Automatic Construction of a Medical Thesaurus

1972 ◽  
Vol 11 (02) ◽  
pp. 104-113 ◽  
Author(s):  
M. WOLFF-TEHROINE ◽  
D. RIMBERT ◽  
B. ROUAULT
Keyword(s):  
Statistical Methods ◽  
Automatic Processing ◽  
Medical Data ◽  
Point Of View ◽  
Similarity Criteria ◽  
Clustering Techniques ◽  
Practical Point ◽  
Automatic Construction ◽  
Selection Stage

The building up of a metalanguage for the automatic processing of medical data (records or literature) requires, after a selection stage, a structuration stage. For this purpose, various clustering techniques were investigated out of a range of similarity criteria.The interest in these criteria and their importance are discussed from a theoretical and a practical point of view.The role played by similarity criteria in obtaining the environment of each term and the use of this environment for the retrieval are discussed.

Alexithymia and the Automatic Processing of Affective Information

2007 ◽  
Author(s):  
Karolina Czernecka ◽  
Michal Wierzchon ◽  
Dariusz Asanowicz
Keyword(s):  
Automatic Processing ◽  
Affective Information

Simultaneous Attentive and Automatic Processing of Visually Presented Characters

1992 ◽  
Author(s):  
Richard Shiffrin ◽  
Asher Cohen ◽  
Michael Fragassi
Keyword(s):  
Automatic Processing

Clustering techniques for thyroid nodules malignancy inference in the era of personalized medicine

2020 ◽  
Author(s):  
Andrea Giani ◽  
de Souza Patricia Borges ◽  
Stefania Bartoletti ◽  
Flavio Morselli ◽  
Andrea Conti ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Thyroid Nodules ◽  
Clustering Techniques

A State of Art Approaches on Energy Efficient Clustering Techniques in WSN

2019 ◽  
Vol 7 (3) ◽  
pp. 50-54
Author(s):  
N. Thilagavathi ◽  
Christy Wood ◽  
V. Hemalakshumi ◽  
V. Mathumiithaa
Keyword(s):  
Energy Efficient ◽  
Clustering Techniques ◽  
Energy Efficient Clustering ◽  
State Of Art
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