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Wyświetlanie 1-3 z 3
Tytuł:
Cluster analysis on the example of work data of the National Power System. Part 2, Research and selected results
Autorzy:
Tchórzewski, Jerzy
Kania, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/2052268.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
cluster analysis
National Power System
MATLAB
Simulink
Ward's algorithm
self-organizing two-dimensional maps
Opis:
The work is a continuation of the article under the same main title and subtitle Part 1. Comparative study of methods and conditions. This article concerns the cluster analysis, which was carried out on the example of data concerning the operation of the National Power System, namely the total generations of nCDGUs and CDGUs listed by PSE Operator. Two algorithms were used to obtain the results of the cluster analysis, i.e. the Ward algorithm and the algorithm of self-organizing twodimensional topographic maps. The obtained results were interpreted and their discussion and interpretation were conducted.
Źródło:
Studia Informatica : systems and information technology; 2020, 1-2(24); 5-23
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cluster analysis on the example of work data of the National Power System. Part 1, Comparative study of methods and conditions
Autorzy:
Tchórzewski, Jerzy
Kania, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/1819255.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
cluster analysis
National Electric Power System
MATLAB and simulink environment
ward's algorithm
self-organizing maps
Opis:
The paper presents the results of the research on the comparative study of the methods of cluster analysis and conditions, which was carried out from the point of view of their use, on the example of data concerning the operation of the National Power System. Two algorithms were used for the clustering analysis, i.e. the Ward algorithm and the algorithm of self-organizing two-dimensional maps. Cluster analysis was preceded by a review of hierarchical and non-hierarchical methods of data analysis and a description of the prepared experiment. The obtained results were interpreted. The work consists of two parts published under the same main title with different subtitles. This part 1 presents the results of the conducted review of selected methods of cluster analysis and the research conditions resulting from the adopted data on the operation of the National Power System. Part 2 presents the cluster analysis process and selected research results and their discussion.
Źródło:
Studia Informatica : systems and information technology; 2019, 1-2(23); 25--41
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of data quoted on the Day-Ahead Market of TGE S.A. using Statistics and Machine Learning Toolbox
Autorzy:
Tchórzewski, Jerzy
Longota, Bartłomiej
Powiązania:
https://bibliotekanauki.pl/articles/2201615.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
artificial neural network
cluster analysis
Day-Ahead Market
k-means method
Matlab and Simulink environment
Statistics and Machine Learning Toolbox
Ward’s method
Opis:
The publication contains the results of research in the field of cluster analysis carried out using data quoted on the Day-Ahead Market of TGE S.A. Two methods were used in the analysis, one hierarchical known as the Ward’s method, and the other non-hierarchical - the k-means method. Many interesting research results have been obtained, which are illustrated, among others, in in the form of dendrograms, silhouette graphs and graphs in the form of clusters. Data on the volume and the volumeweighted average price of electricity were examined for various types of quotations: fixing 1, fixing 2 and continuous quotations. The research was carried out in the MATLAB and Simulink environments using a library called Machine and Statistics Learning Toolbox. Selected test results were interpreted.
Źródło:
Studia Informatica : systems and information technology; 2022, 2(27); 49--74
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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