The paper at hand portrays the merging of AI with the realm of renewable energy in the view of sustainable power. It considers the discussion on the advancements made in the state of the art as to the country-wise situation in elaboration to depict how AI is integrated in the betterment of renewable energy feasibility, effectiveness, and levels of grid integration. In applications concerning solar potentials, AI algorithms - primarily when unsupervised - are implemented in such systems to actualize the maximum effective passive solar potential. The next research demonstrates how these algorithms, when used with large datasets to achieve efficiency, may be employed toward the prediction and perhaps aid in the avoidance of recombination events in solar cells. This research will use artificial intelligence (AI) to band gap engineering in order to improve solar absorption efficiency. Gradient Boosting and Random Forest, belonging to the family of Machine Learning techniques, will be used for simulating: the way these patterns are associated between the patterns of solar irradiation, climatic variables, and energy output. It concludes with a view of how the predictive power of AI will shape a future in energy that is resilient and sustainable. (Figure. 1)
Overview of the Impact of Artificial Intelligence on the Future of Renewable Energy
Manganelli M.
2024-01-01
Abstract
The paper at hand portrays the merging of AI with the realm of renewable energy in the view of sustainable power. It considers the discussion on the advancements made in the state of the art as to the country-wise situation in elaboration to depict how AI is integrated in the betterment of renewable energy feasibility, effectiveness, and levels of grid integration. In applications concerning solar potentials, AI algorithms - primarily when unsupervised - are implemented in such systems to actualize the maximum effective passive solar potential. The next research demonstrates how these algorithms, when used with large datasets to achieve efficiency, may be employed toward the prediction and perhaps aid in the avoidance of recombination events in solar cells. This research will use artificial intelligence (AI) to band gap engineering in order to improve solar absorption efficiency. Gradient Boosting and Random Forest, belonging to the family of Machine Learning techniques, will be used for simulating: the way these patterns are associated between the patterns of solar irradiation, climatic variables, and energy output. It concludes with a view of how the predictive power of AI will shape a future in energy that is resilient and sustainable. (Figure. 1)I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
