An Innovative Solar Power Forecasting on Grid Using Feed Forward Neural Network Simulation to Better Teach the Photovoltaic Generation to Students

Authors

  • Unit Three Kartini Department Electrical Engineering, Universitas Negeri Surabaya
  • Parama Diptya Widayaka Department Electrical Engineering, Universitas Negeri Surabaya
  • L. Endah Cahya Ningrum Department Electrical Engineering, Universitas Negeri Surabaya
  • Tri Wahyu Yulianto Department Electrical Engineering, Universitas Negeri Surabaya
  • Masviki Agam Department Electrical Engineering, Universitas Negeri Surabaya

Keywords:

Solar, Forecasting, Photovoltaic generation, Feed Forward Neural Network, Simulation.

Abstract

The research deals with a novelty approach to science and education regarding power electrical forecasting photovoltaic generation. This teaching method is based on Solar Power Electrical Forecasting on grid using Feed Forward Neural Network Simulation. This apparatus is able to forecast the power electrical which the production photovoltaic generation household scale during one hour ahead. The power energy production is then estimated with different orientations and based on meteorology data. The Solar Power Electrical Forecasting on grid using Feed Forward Neural Network Simulation provides students with the unique opportunity to learn, in a playful manner, the fundamental principles of photovoltaic generation on grid household scale. Several examples of practical work are detailed to give an accurate appreciation of many simulations in photovoltaic generation. A lot of simulations could be studied such as the implementation of global solar irradiance, and the electrical characterization of solar cells with various technologies based on meteorology data.

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Published

2024-02-01

Issue

Vol. 5 (2023)

Section

Articles