Implementation of Mamdani and Sugeno Method for Load Forecasting: A Case Study of Malang City

Authors

  • Yusuf Ismail Nakhoda Department of Electrical Engineering, National Institute of Technology, Malang, Indonesia.
  • Ni Putu Agustini Department of Electrical Engineering, National Institute of Technology, Malang, Indonesia.
  • Ikhzanul Bagus Ariyanto Department of Electrical Engineering, National Institute of Technology, Malang, Indonesia.
  • Abraham Lomi Department of Electrical Engineering, National Institute of Technology, Malang, Indonesia.

Keywords:

Long-Term Electric Load Forecasting, Fuzzy Logic, Mamdani and Sugeno, MAPE,

Abstract

The growth of energy consumption in many developing countries has exceeded the projection, and therefore, the uncertainty of energy forecasting increases. Variables such as economic growth, population, and efficiency standards, coupled with other factors inherent in the mathematical progression of forecasting models, make accurate projections difficult. The objective of load forecasting is to predict the electrical power required to meet short, medium, or long-term demand, power consumption planning and operations. Large energy consumption over a period of time should be predicted and calculated specifically in order to plan and manage the operation of the power plant. Load forecasting allows utility companies to plan well for future consumption or load demand and also minimize risks for utility companies. Therefore, an accurate method is needed to forecast loads, such as accurate models that take into account factors that affect load growth over several years. In this paper, Mamdani and Sugeno's methods for predicting electrical load forecasting are implemented. The supporting data used in this paper was adopted from Polehan and Blimbing Substations from 2011- 2020. As a result, the average MAPE value according to the Mamdani and Sugeno methods are respectively 2.49% and 1.16%. It can be concluded that the Sugeno method fulfilled the load forecasting from Mamdani.

References

Khair, A., (2011), Peramalan Beban Listrik Jangka Pendek Menggunakan Kombinasi Metode Autoregressive Integrated Moving Average (ARIMA) began Regresi Linear Antara Suhu dan Daya Listrik, Program Studi Teknik Elektro, Fakultas Teknik Universitas Indonesia.

Massarang; Erni; Agus. (2014). Peramalan Beban jangka Panjang Sistem Kelistrikan Kota Palu Menggunakan Metode Logika Fuzzy. Jurnal EECCIS Vol 8. No 2. Fakultas Teknik Universitas Brawijaya, Malang.

Leonard L. Grigsby (2012). Electric Power Generation, Transmission, and Distribution (3rd Ed.). CRC Press.

D K Ranaweera, N F Hubele and GG Karady (1996). Fuzzy Logic For Short Term Load Forecasting. Electrical Power & Energy Systems Vol. 18 No. 4, pp. 215-222. Arizona State University, Tempe, Arizona, USA.

Kyung-Bin Song, Young-Sik Baek, Dug Hun Hong and Gilsoo Jang (2005). Short-Term Load Forecasting For the Holidays Using Fuzzy Linear Regression Method. IEEE Transactions On Power Systems. Vol 20, No.1. University Seoul. South Korea.

Jagadish H. Pujar (2010). Fuzzy Ideology Based Long Term Load Forecasting. World Academy of Science, Engineering, and Technology. International Journal of Computer, Electrical, Automation, Control and Information Engineering. Vol. 4. No.4. BVB College of Engg & Tech. India.

Swaroop R and Hussein Ali Al Abdulqader (2012). Load Forecasting for Power System Planning using Fuzzy-Neural Networks. Proceedings of The World Congress on Engineering and Computer Science. Vol. 1. WCECS. San Fransisco. USA.

Kyung-Bin Song, Young-Sik Baek, Dug Hun Hong and Gilsoo Jang (2005). Short-Term Load Forecasting For the Holidays Using Fuzzy Linear Regression Method. IEEE Transactions On Power Systems. Vol 20, No.1. University Seoul. South Korea.

Downloads

Published

2018-05-31

How to Cite

Nakhoda, Y. I., Agustini, N. P., Ariyanto, I. B., & Lomi, A. (2018). Implementation of Mamdani and Sugeno Method for Load Forecasting: A Case Study of Malang City. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-3), 97–103. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4200