Analysis of data quoted on the Day-Ahead Market of TGE S.A. using Statistics and Machine Learning Toolbox

• Autorzy: Tchórzewski Jerzy , Longota Bartłomiej

Identyfikatory

DOI

10.34739/si.2022.27.03

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

artificial neural network; cluster analysis; Day-Ahead Market; k-means method; Matlab and Simulink environment; Statistics and Machine Learning Toolbox; Ward’s method

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2022
• Tom: Vol. 2(27)
• Strony: 49--74
• Opis fizyczny: Bibliogr. 22 poz., rys., tab., wykr.

Twórcy

autor

Tchórzewski Jerzy

• Siedlce University of Natural Sciences and Humanities Faculty of Exact and Natural Sciences Institute of Computer Science ul. 3 Maja 54, 08-110 Siedlce, Poland

• https://orcid.org/0000-0003-2198-7185

autor

Longota Bartłomiej

• Studentat University of Natural Sciences and Humanities Faculty of Exact and Natural Sciences Institute of Computer Science ul. 3 Maja 54, 08-110 Siedlce, Poland

• https://orcid.org/0000-0002-4011-3056

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).