A systematic study on shaping the future of solar prosumage using deep learning

One of the core necessities for development in the modern world is a clear access to energy. Therefore, places that lack access to energy face serious challenges on their ability to progress effectively. In the bid to progress, nations have utilized conventional energy sources for decades leading to plummeting natural oil levels and increasing global temperatures, not to mention climate change. Energy transitions have begun to catch up but the world is far from creating a decisive decline in global emissions. Next to transportation, electricity generation burns the highest amount of fossil fuels today. The losses incurred during transmission and distribution exacerbate existing problems. One viable solution is to interlink local sustainable energy generation plants to existing grids. As of 2019, 583.5 GW of operation photovoltaic energy supplies our demands. Solar prosumage development promises to boost this rise in PV usage. However, it is difficult as it is to manage conventional grids let alone interlinked smart grids. Unlike classical approaches to developing electrical grid lines, modern systems demand much more in terms of tangible resources, statistical analysis and computational techniques. This level of complexity has inspired engineers to develop Artificial Intelligence techniques that can develop deep neural networks that grow robustly, while maintaining flexibility in handling non-linear complex relationships within large sets of data. In this paper, we provide a systematic study of existing AI solutions for smart solar grids and discuss the possible challenges associated with implementing this technology in near future.