Anisotropic Active Single Axis Solar Tracker using Machine Learning

Abstract

Background: Solar is always promising one for largely used renewable energy source over worldwide. After the era of fuels, the solar energy paved its way due to the decrease of resources and the world in need of a clean and harmless energy. Global warming and climate change are the outcome of  increasing carbon dioxide from the power plants and are widely seen to be one of the most serious problem as it contributes to the danger of environmental issues and so today the world is making its step for the SDG goals which help the world to be reborn and problem free within 2030.By keeping this in mind, in this project we are developing the prototype that supports the SDG goal 7 that provides CLEAN AND AFFORDABLE ENERGY and the energy we chose is the Solar energy. Findings: Nowadays many people use solar energy for then and there activities. Offices, homes, industries use solar energy for regular  activities and works and thereby saving lot of costs. Several automations rose to extract maximum power from the radiations of the sun and thus came the technique of rotation of solar panel thereby increasing the extraction like the sunflower that follows the sun. Though Solar tracker extract maximum power it also  has some limitations like cloud factor and dust and climate change makes the solar energy extraction a bit low. Dual Axis tracker (i.e. E to W and also N to S ) is the best model for extracting maximum power yet it costs about $26000 and that it high cost and makes not affordable for everyone. Methods: Thus in this project, we have developed a prototype based on single axis solar tracker (direction wise) by introducing advanced technologies like machine learning to identify the degree of radiation falls on panel to extract maximum power. This new method will be efficient and makes us explore new opportunities to identify the horizons of solving the problems in solar energy tracking.