Optimization of Renewable Energy From Solar Panels For Environmental Monitoring Using Arduino

I Wayan Sugianta Nirawana; Fahmi Romisa; I Gede Karang Komala Putra; Nouval Aulia Rahman; Ayisa Rahmadani Fitria

doi:10.30812/ijecsa.v4i1.4828

I Wayan Sugianta Nirawana Universitas Mulawarman, Samarinda, Indonesia
Fahmi Romisa Universitas Mulawarman, Samarinda, Indonesia
I Gede Karang Komala Putra Universitas Bali Internasional, Denpasar, Indonesia
Nouval Aulia Rahman Universitas Mulawarman, Samarinda, Indonesia
Ayisa Rahmadani Fitria Universitas Mulawarman, Samarinda, Indonesia

DOI: https://doi.org/10.30812/ijecsa.v4i1.4828

Keywords: Solar Panels, Solar Tracker, Renewable Energy, Internet of Things, Fuzzy Logic

Abstract

Renewable energy is an alternative energy in the midst of the issue of fossil-based energy running out. One of these alternative energies is solar panels that generate electricity to achieve environmentally friendly energy. The problem researched in this study is the need to optimize or energy efficiency of solar panels. The type of research is development research with a prototype model. The purpose of this study is to optimize the efficiency of the solar panel tracking system equipped with artificial intelligence and sensors to monitor the environment based on the internet of things. This research method is an integration of fuzzy logic internet of things for tracking solar panels based on light sensor input. The use of solar panels using polycrystalline types, and the addition of temperature and humidity sensors is important to utilize. The result of this study is that the highest efficiency is obtained at 9.96%, meaning that the energy received by the system is 9.96% into useful power that is lost. In conclusion, solar panels are highly dependent on weather conditions and the focal point of solar energy capture. The implication of this study is that the improvement of environmentally friendly energy efficiency, although not too high, can be further developed. Recommendations for further research suggest that data collection be carried out in sunny weather, and improved using solar panels with a larger capacity and energy storage from batteries. The use of machine learning algorithms can be an alternative to study large amounts of solar panel data.

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