How Machine Learning can support further development in solar

New and innovative technology is being introduced into the industry at an accelerated rate. For clean energy, the new technology represents a number of new and exciting opportunities.  Creating the most efficient and cost-effective solar systems requires utilising the best technology available. Industry professionals are now exploring a number of new systems that could be used to radically transform the solar market.

Machine Learning in the renewable energy market

Within the US, China, and Europe, researchers are working effortlessly to design new and efficient solar modules. Scientists are experimenting with a range of properties to enhance base efficiency, the economic potential of new solar PV panels is becoming more viable.

According to CleanTechnica, scientists are focusing their efforts on trialling a significant number of combinations in laboratories before taking to market. Industry experts believe these studies are vital for delivering machine learning into the renewable energy market. A number of researchers are using perovskite solar panels due to the efficiency potential clearly shown in small scale studies. Other researchers are focusing their attention on applying machine learning systems to develop a thin-film organic solar panel. While not being as efficient as conventional panels, they do have one particular advantage. They are capable of generating higher levels of electricity compared to other materials. 

These models still require more attention but machine learning is supporting researchers in developing materials that offer more efficiency and the potential to generate products at a more affordable price.  Having access to data and insights that can be shared within the industry will enable accelerated innovation within the solar industry. Using machine learning processes, solar systems can be optimised and the rate of arrival of new and efficient solar products will likely increase.