Implementation of Fuzzy Sugeno Method for Power Efficiency

Riza Alfita, Durrotul Mamluah, Miftachul Ulum, Rosida Vivin Nahari

Important Links

Impact Factor

New

Computer Science Directory. We are listed under Publication Journals category

Implementation of Fuzzy Sugeno Method for Power Efficiency
( Vol-4,Issue-9,September 2017 )

Author(s):

Riza Alfita, Durrotul Mamluah, Miftachul Ulum, Rosida Vivin Nahari

Keywords:

Electrical Power, Efficiency, Fuzzy Sugeno, ECI, SEC

Abstract:

Energy is one of the basic needs for human being. One of the most vital energy sources is electricity. Electricity is a type of energy that sustains survival of human being, more particularly in industrial sector. Efficiency in industrial sector refers to a state where electricity is used to as little as possible to produce the same amount of product. The case study was conducted in marine commodity sector, anchovy and jellyfish supplier. The supplier was classified as SME that installed 33,000 VA electric powers (B2). The data were in the form of energy consumption intensity (ECI) and specific energy consumption (SEC) to determine the energy efficiency level. The objective of the study was to classify the efficiency level of electricity consumption using Sugeno Fuzzy method. The findings of the study were 1) the average ECI between January, 2016 and April, 2017 was 1,949 kWh/m2; it was classified as efficient; 2) the average SEC at the same period was 126,108 kWh/ton; it was classified as excessive. Sugeno Fuzzy logic was implemented to determine efficiency level of electricity in this company. Based on the average ECI and SEC, the electricity consumption of the company was categorized as excessive with FIS Sugeno output of 0.803.

DOI:

10.22161/ijaers.4.9.1

Paper Statistics:

Cite this Article:

Share:

References:

[1] Raharjo, B.A. (2014) “Studi Analisis Konsumsi dan Penghematan Energi di PT. P.G. Krebet Baru I”, Tugas Akhir Teknik Elektro, Universitas Brawijaya, Malang.
[2] Harmoko, I. W., & Nazori, A. Z., (2014) “Prototipe Model Prediksi Peluang Kejadian Hujan Menggunakan Metode Fuzzy Logic Tipe Mamdani dan Sugeno”, Jurnal TELEMATIKA MKOM, Vol. 5, 1.
[3] Eugene. C, Hanapi, & Gunawan. Drs. Ir., (1993). Mesin dan Rangkaian Listrik, Edisi Keenam, ITB, Bandung.
[4] Wahid, A., (2014) “Analisis Kapasitas dan Kebutuhan Daya Listrik untuk Menghemat Penggunaan Energi Listrik di Fakultas Teknik Universitas Tanjungpura”, Jurnal Teknik Elektro Universitas Tanjungpura, Vol. 2, 1.
[5] Laksono, H.D., & Arief, A., (2013) “Penggunaan Logika Fuzzy Clustering untuk Peramalan Kebutuhan Energi Listrik Jangka Panjang di Provinsi Sumatera Barat”, Jurnal Teknologi Informasi & Pendidikan. Vol 6.
[6] Santosa, H., (2014) “Aplikasi Penentuan Tarif Listrik Menggunakan Metode Fuzzy Sugeno”, Jurnal Sistem Informasi Bisnis. Vol 01.
[7] Naba, Agus. Dr. Eng., (2009) Belajar Cepat Fuzzy Logic Menggunakan MATLAB, Andi Offset, Yogyakarta.
[8] Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, (1), 116-132.
[9] Melin, P., Mancilla, A., Lopez, M., & Mendoza, O. (2007). A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting. Applied Soft Computing, 7(4), 1217-1226.
[10] Li, H., Wang, J., Du, H., & Karimi, H. R. (2017). Adaptive Sliding Mode Control for Takagi-Sugeno Fuzzy Systems and Its Applications. IEEE Transactions on Fuzzy Systems.
[11] Pak, J. M., Ahn, C. K., Lee, C. J., Shi, P., Lim, M. T., & Song, M. K. (2016). Fuzzy horizon group shift FIR filtering for nonlinear systems with Takagi–Sugeno model. Neurocomputing, 174, 1013-1020.
[12] Jia, Q., Chen, W., Zhang, Y., & Li, H. (2015). Fault reconstruction and fault-tolerant control via learning observers in Takagi–Sugeno fuzzy descriptor systems with time delays. IEEE Transactions on industrial electronics, 62(6), 3885-3895
[13] Hassan, L. H., Moghavvemi, M., Almurib, H. A., & Muttaqi, K. M. (2016, October). Damping of low-frequency oscillations using Takagi-Sugeno Fuzzy stabilizer in real-time. In Industry Applications Society Annual Meeting, 2016 IEEE (pp. 1-7). IEEE.
[14] Talla, J., Streit, L., Peroutka, Z., & Drabek, P. (2015). Position-based TS fuzzy power management for tram with energy storage system. IEEE Transactions on Industrial Electronics, 62(5), 3061-3071.
[15] Kumar, A., Jones, D. D., Meyer, G. E., & Hanna, M. A. (2015). A Fuzzy Inference System (FIS) and Dimensional Analysis for Predicting Energy Consumption and Mean Residence Time in a Twin‐Screw Extruder. Journal of Food Process Engineering, 38(2), 125-134.