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Journals / Düzce Üniversitesi Bilim ve Teknoloji Dergisi / 2022 / Cilt: 10 - Sayı: 2

Two-Stage Clustering Approach for the Household Electricity Load Profiles by Fuzzy Logic and Neural Network Techniques

Evsel Elektriksel Yük Profilleri için Bulanık Mantık ve Yapay Sinir Ağları Teknikleri ile İki-Kademeli Kümeleme Yaklaşımı

Uğur Buğra ETLİK
Yavuz EREN
Pages
981–990
Publication Date
2022-10-12
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed strategy has been simulated by MATLAB® and the related results are presented.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, household electricity load profile (LP) clustering problem is addressed.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:The proposed strategy has been simulated by MATLAB® and the related results are presented.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kümeleme · Sugeno Bulanık Mantık · Öz-düzenleyici Haritalı Yapay Sinir Ağları

Abstract

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.

Özet

Bu çalışmada, evsel elektriksel yük profili (YP) kümeleme problemi ele alınmıştır. YP kümeleme analizleri ile güç sistemlerinin kararlılığını sağlamada yararlanılan talep veya arz yönetimi stratejilerinin icrasında gerekli olan tahmini son kullanıcı YP tiplerinin elde edilmesi sağlanabilmektedir. YP tüketim dinamikleri hem teknik hem de sosyal unsurların etkileri ile şekillenmektedir. Bu bakımdan, YP dinamik davranışını anlamlandırmak zor bir problemdir. Bu çalışmada, bahsedilen bu problemin çözümü için iki ayrı aşamada sırasıyla Sugeno bulanık- mantık (SBM) ve öz-düzenleyici haritalı yapay sinir ağları (ÖDHYSA) tekniklerinin uygulandığı çözüm önerilmiştir. İlk olarak, YP veri seti ev halkı sayısı temelli aile tipleri üzerinden modellenen referans yük tipleri dikkate alınarak SBM tekniği ile sınıflandırılmıştır. Daha sonra, evde hâlihazırda bulunan hane halkının zaman bazlı ağırlıklandırılmış şekliyle de belirleyici bir giriş verisi olduğu düşünülerek ÖDHYSA tekniği uygulanıp kümeleme sonuçları iyileştirilmiştir. Önerilen stratejinin benzetim çalışması MATLAB® ortamındagerçekleştirilip ilgili sonuçlar sunulmuştur.

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Cite This Article

ETLİK, U. B., EREN, Y. (2022). Two-Stage Clustering Approach for the Household Electricity Load Profiles by Fuzzy Logic and Neural Network Techniques. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Cilt: 10 - Sayı: 2, 981–990. https://ajindex.com/articles/493849

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