Technology Sun Tracking System for Solar Power Plants Base on Recurrent Neural Networks

DOI

10.2991/aer.k.201221.038How to use a DOI?

Keywords

Solar Panels, Sun Tracking, Recurrent Neural Network

Abstract

Solar energy is one alternative of renewable sources of energy that can be used as a substitute for fossil fuel, which will be eventually depleted. Since Indonesia is located at the equator with abundant supply of sunlight all through the year, the use of solar energy as an alternative source of energy is considered the right decision. However, the movement of the sun throughout the day may reduce the absorption of the solar energy. Thus, the solar panel needs to be equipped with a tracking system to be able to track the sun and get the highest solar energy as possible. There are several steps to trace in this problem the first is detecting the absorption of energy in the solar panel, the second is moving the solar panel in the direction of the sun, and the third is making an estimation if there is a change in time of the day or season. The method that is used to optimize the sun tracking is Recurrent Neural Network (RNN). This method is implemented to help making the best decision for the solar panel movement.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Abdul Haris
AU  - Sri Wahjuni
AU  - Heru Sukoco
AU  - Hendra Rahmawan
AU  - Shelvie Nidya Neyman
AU  - Hengki Sikumbang
AU  - Muhammad Jafar Elly
PY  - 2020
DA  - 2020/12/22
TI  - Technology Sun Tracking System for Solar Power Plants Base on Recurrent Neural Networks
BT  - Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020)
PB  - Atlantis Press
SP  - 223
EP  - 226
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.201221.038
DO  - 10.2991/aer.k.201221.038
ID  - Haris2020
ER  -