A COMPREHENSIVE STUDY OF CONTEMPORARY ADVANCEMENTS IN HYBRID RENEWABLE ENERGY SYSTEMS INCORPORATING ARTIFICIAL INTELLIGENCE METHODOLOGIES

An extremely useable and excellent alternate power source is solar power which can really reduce or it may say cut our dependency on the non-renewable energy sources and destructive fossil fuels. Amongst all renewable energy sources, solar radiation energy source has an important role in various platforms like climate and weather extremes, photosynthesis, hydrological cycles, balancing the radiation and geographic conditions etc that is why it has very important role. Solar radiation (SR) can be anticipated with extraordinary accuracy, and it could be feasible to definitely limit the effect cost related with the advancement of solar energy. In order to predict the advancement of renewable energy frameworks, this study intends to investigate several artificial intelligence applications in relation to various basic forecast benchmark models from literature assessments. Differential conditions, massive PC power, and time requirements are among the many models that are used to regulate or predict energy framework exhibits. Machine learning techniques seem to be among the best options. With a special focus on Artificial Intelligence, the study provides an overview of broad AI philosophies related to renewable energy sources.