Load profile segmentation for electricity market settlement

Name: Load profile segmentation for electricity market settlement
Author: Gunsay, M., Bilir, C., Poyrazoğlu, Göktürk

İsim	Load profile segmentation for electricity market settlement
Yazar	Gunsay, M., Bilir, C., Poyrazoğlu, Göktürk
Basım Tarihi:	2020-09
Basım Yeri	- IEEE
Konu	Load profiling, Clustering, Consumption, Market settlement
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2-s2.0-85094866970
Kayıt Numarası	c8bd79ac-0877-4e29-84d1-5209e9da0dd2
Lokasyon	Electrical & Electronics Engineering
Tarih	2020-09
Örnek Metin	An unsupervised learning method is used to create clusters for electricity load profiles within a group of real customers. A time-series analysis method (hierarchical clustering) is adopted. A case study is conducted with real consumption data from residential, commercial, and industrial consumers to show the effectiveness of the proposed clustering method for load profiling. After the data cleansing, filtering, and normalization processes, the input dataset is divided into several clusters based on their profile differences. Later, various results are obtained to reflect different consumption patterns within a profile group by the selected distance measurement methods such as Euclidean and Dynamic Time Warping. The results obtained in the case study show that the proposed mathematical algorithm can be used to create realistic and scalable profiling subgroups (with percentages of similar consumptions in each cluster) instead of the traditional methods which cluster all profiles in a single big cluster. The proposed algorithm is used for a case study of Turkey; however, this study is adaptable to other European markets.
DOI	10.1109/EEM49802.2020.9221889

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Load profile segmentation for electricity market settlement

Yazar Gunsay, M., Bilir, C., Poyrazoğlu, Göktürk

Basım Tarihi 2020-09

Basım Yeri - IEEE

Konu Load profiling, Clustering, Consumption, Market settlement

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2-s2.0-85094866970

Kayıt Numarası c8bd79ac-0877-4e29-84d1-5209e9da0dd2

Lokasyon Electrical & Electronics Engineering

Tarih 2020-09

Örnek Metin An unsupervised learning method is used to create clusters for electricity load profiles within a group of real customers. A time-series analysis method (hierarchical clustering) is adopted. A case study is conducted with real consumption data from residential, commercial, and industrial consumers to show the effectiveness of the proposed clustering method for load profiling. After the data cleansing, filtering, and normalization processes, the input dataset is divided into several clusters based on their profile differences. Later, various results are obtained to reflect different consumption patterns within a profile group by the selected distance measurement methods such as Euclidean and Dynamic Time Warping. The results obtained in the case study show that the proposed mathematical algorithm can be used to create realistic and scalable profiling subgroups (with percentages of similar consumptions in each cluster) instead of the traditional methods which cluster all profiles in a single big cluster. The proposed algorithm is used for a case study of Turkey; however, this study is adaptable to other European markets.

DOI 10.1109/EEM49802.2020.9221889