Using Machine Learning & Neural Networks to Maximize Solar Energy Globally

Scientists are always on the lookout for ways to make our world a better place, and one area they’re focusing on is solar energy. One idea in this area is to make solar cells more efficient by concentrating more solar light onto them. While investigating this recently, a group of scientists at the Cavendish Laboratory and AMOLF (Amsterdam NL) have found that improving solar cells efficiency in this way is harder than we might think but have discovered other avenues by which it might be possible to improve solar energy capture anywhere on the planet.

The researchers were interested in finding out if solar cells, devices that turn sunlight into electricity, could be tweaked to perform better in different parts of the world, where concentration of solar light may be higher. To examine this, they used machine learning models and neural networks (AI) to understand how the sun’s radiation would behave in different spots on Earth.

Furthermore, the researchers advocate the use of patterning the solar capture devices with the aim to optimise their arrangement for maximum sunlight absorption. This approach holds the potential to improve the design of solar arrays, increasing their effectiveness in harnessing solar energy.