ANALISIS PROFIL BEBAN LISTRIK MENGGUNAKAN TEKNIK CLUSTERING

Damayanti, Ranti (2016) ANALISIS PROFIL BEBAN LISTRIK MENGGUNAKAN TEKNIK CLUSTERING. S1 thesis, Universitas Pendidikan Indonesia.

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Official URL: http://repository.upi.edu

Abstract

Data mining merupakan salah satu teknik pengolahan data dengan menggali berbagai informasi dari sekumpulan data yang tersimpan. Setiap hari, konsumsi beban listrik tercatat oleh PLN, biasanya dalam interval waktu 15 atau 30 menit. Paper ini menggunakan teknik clustering yang merupakan salah satu teknik data mining untuk menganalisis profil beban listrik selama tahun 2014. Tiga buah metode teknik clustering dibandingkan, yaitu K-Means (KM), Fuzzy C-Means (FCM), dan K-Harmonics Means (KHM). Hasilnya diketahui bahwa KHM merupakan metode yang paling tepat dalam mengelompokkan profil beban listrik. Jumlah cluster optimum ditentukan menggunakan Davies-Bouldin Index. Dengan mengelompokkan profil beban, analisis variasi permintaan dan estimasi kehilangan energi dari pola profil beban yang serupa dalam satu kelompok dapat dilakukan. Dari kelompok profil beban listrik dapat diketahui cluster load factor dan range of cluster loss factor yang dapat membantu menemukan kisaran nilai koefisien untuk estimasi kehilangan energi tanpa melakukan studi aliran beban.;---Data mining is one of the data processing techniques to collect information from a set of stored data. Every day, the consumption of electricity load recorded by PLN, usually at intervals of 15 or 30 minutes. This paper uses clustering techniques, which is one of data mining techniques to analyze the electrical load profiles during 2014. The three methods of clustering techniques were compared, namely K-Means (KM), Fuzzy C-Means (FCM), and K-Means Harmonics (KHM). The result is known that KHM is the most appropriate method to classify the electrical load profile. The optimum number of clusters is determined using the Davies-Bouldin Index. By grouping the load profile, demand variation analysis and estimation of energy loss from the group of load profile with similar pattern can be done. From the group of electric load profile can be known cluster load factor and a range of cluster loss factor that can help to find the range of values of coefficients for the estimated loss of energy without performing load flow studies.

Item Type: Skripsi,Tesis,Disertasi (S1)
Additional Information: No. Panggil : S TE DAM a-2016; Pembimbing : I. Ade Gafar, II. wawan Purnama.
Uncontrolled Keywords: Profil beban listrik; k-means; fuzzy c-means; k-harmonic means; davies-bouldin index; loss factor; load factor, electric load profile; k-means; fuzzy c-means; k-harmonic means; davies-bouldin index; loss factor; load factor.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Fakultas Pendidikan Teknologi dan Kejuruan > Jurusan Pendidikan Teknik Elektro
Depositing User: Mr mhsinf 2017
Date Deposited: 31 Aug 2017 07:53
Last Modified: 31 Aug 2017 07:53
URI: http://repository.upi.edu/id/eprint/25553

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