Integration of Renewable Hybrid Energy System: A State of Art

The presence of sunshine, air, and other resources on Earth must be used in a manner that promotes human well-being while safeguarding the environment and its inhabitants. The use of sunlight and air as a significant Renewable Energy (RE) source has been a critical area of innovation or new product development in recent years. But integrating AI and ML with renewable energy can be a breakthrough for the whole world. Artificial intelligence (AI) and machine learning (ML) have the potential to significantly contribute to the effectiveness, efficiency, and cost-cutting in the production of solar and biodiesel energy. The output of solar energy may be analyzed and expected based on weather patterns using ML algorithms, and the distribution of biodiesel fuels might benefit from AI's assistance in improving the supply chain. Artificial intelligence and machine learning will help the renewable energy industry expand, which will have a beneficial effect on the planet. So, the major focus of this paper is Artificial neural networks (ANN). The primary emphasis of the introductory section on ANNs in Renewable Energies is on their usage in Solar and Biodiesel. In the realm of Renewable Energies, ANNs have shown to be invaluable instruments for the prediction, control, and optimization of a wide range of systems. Advanced technologies like, Photovoltaic power prediction, maximum power point tracking, and optimum size of photovoltaic systems are just some of the ways that ANNs have been put to use in the Solar Energy sector. Artificial neural networks (ANNs) have been employed in the biodiesel industry for a variety of purposes, including the forecasting of fuel qualities, the improvement of the transesterification process, and the forecasting of engine performance. These examples show how ANNs may be put to good use in the Renewable Energy sector, where it can address a wide range of problems in an efficient and effective manner