Two-Stage Clustering Approach for the Household Electricity Load Profiles by Fuzzy Logic and Neural Network Techniques
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Dosyalar
Tarih
2022
Yazarlar
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this paper, household electricity load profile (LP) clustering problem is addressed. LP clustering analysis has been utilized as predicted end-user LPs for demand or supply management strategies to maintain the stability of the power systems. The consumption dynamics of the LPs are formed by the combinations of technical and social factors. Hence, discovering the dynamic patterns of the LPs has been a challenging problem. For this problem, we have offered successive applications of Sugeno fuzzy-logic (SFL) and self-organizing map neural network (SOMNN) techniques. Firstly, the data sets of the LPs are clustered by fuzzy logic approach by the reference models which are generated with the common family-types per persons. Then, considering the extra input of the weighted occupancy profiles, SOMNN is performed to improve the clustering result according to the dataset. The proposed strategy has been simulated by MATLAB® and the related results are presented.
Açıklama
Anahtar Kelimeler
Clustering, Sugeno Fuzzy Logic, Self-Organizing Map Neural Network, Household Load Profiles Kümeleme, Sugeno Bulanık Mantık, Öz-düzenleyici Harita Yapay Sinir Ağları, Evsel Yük Profilleri
Kaynak
Düzce Üniversitesi Bilim ve Teknoloji Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
10
Sayı
2
