Photovoltaic’s characteristics modelling based on fuzzy time series

Authors

  • Fortinov Akbar Irdam Under Graduate Student, Department of Mechanical Engineering
  • Dani Rusirawan Department of Mechanical engineering Institut Teknologi Nasional (ITENAS), Bandung – INDONESIA

Keywords:

Renewable Energy, Solar Energy, Photovoltaic, Fuzzy Time Series,, Margin of Error

Abstract

Recently, fulfillment of energy needs is predominated by utilization of fossil energy. Fossil energy is limited resources and less eco-friendly. Based on that, researches began to develop clean energy based on renewable resources, solar as main energy’s source for humankind’s live is one of them. The potential of solar energy in Indonesia is estimated 207,8 GWp, the largest around other renewable resources. Due to sunshine year-round, Indonesia become suitable place to develop solar-based energy technology, especially photovoltaic (PV). PV is device that is converted solar energy into electricity. PV has its own characteristics, such as ambient temperature, irradiation, humidity, and wind velocity as input parameters and cell temperature, voltage, current, and power as output parameters. All of characteristics can be obtained by direct measurement which need more device and waste time. To simplify in understanding all of these parameters, modelling is needed. This research will be shown PV characteristics modelling based on fuzzy time series (FTS). FTS is one of fuzzy logic method to predict future data based on historical data. The focus of modelling is output parameters, such as voltage (V), current (I), and power (P). The collected data will be divided into some classes with same intervals before fuzzy sets are created. After that, fuzzy logic processes will be done to obtain the modelling results. Margin of Error is obtained by compare of the modelling results and collected data. They are 3,9% for voltage; 24,7% for current at intervals of 0,05 and 34,8% at intervals of 0,2; 21,5% for power at intervals of 7 and 34,8% at intervals of 20.

References

BPPT: Indonesia Energy Outlook. 2018. Outlook Energi Indonesia 2018 : Energi Berkelanjutan Untuk Transportasi Darat. Vol. 134.

Chen, Shyi Ming. 1996. “Forecasting Enrollments Based on Fuzzy Time Series.” Fuzzy Sets and Systems 81:311–19.

Haris, M. Syauqi. 2010. “Implementasi Metode Fuzzy Time Series Dengan Penentuan Interval Berbasis Rata-Rata Untuk Peramalan Data Penjualan Bulanan.” (September 2017):1–8.

Irsyad, Achmad Rofi, Byunggi Kim, Doan Hong Duc, Saiful Hasmady Bin Abu Hassan, and Kazuyoshi Fushinobu. 2018. “Numerical Study of Heat Transfer and Chemical Kinetics of Solar Thermochemical Reactor for Hydrogen Production.” AIP Conference Proceedings 1984(August).

Jr., Harry K. Charles. 1982. “Solar Photovoltaic Energy Systems.” Pp. 663–711 in Handbook of Energy Techology & Economics, edited by R. A. Meyers. John Wiley & Sons.

Severiano, Carlos A., Petrônio C. L. Silva, Hossein Javedani Sadaei, and Frederico Gadelha Guimarães. 2017. “Very Short-Term Solar Forecasting Using Fuzzy Time Series.” IEEE International Conference on Fuzzy Systems (July).

Song, Qiang and Brad S. Chissom. 1993. “Forecasting Enrollments with Fuzzy Time Series - Part 1.” Fuzzy Sets and Systems 54:1–9.

Published

2021-04-22

Issue

Section

FoITIC 2020