How machine learning could advance solar power technology

Where clean energy intercepts with technology, the need to integrated advanced computing is the key to opening new opportunities. The endless journey to discovering a viable return on investment in renewable energy means ensuring absolute optimisation and the best possible method of deployment for all panels and systems. So how exactly can we achieve the level of standards we realistically need to make a global transformation for the sector?

Enter machine learning for renewable energy

Across the US and China, researchers are hard at work creating and experimenting with new solar panel modules. Dabbling with various chemistries to assess how base efficiency can be improved, the economic viability of these panels is becoming more and more clear.

According to CleanTechnica, these researchers are utilising hundreds of thousands of combinations in their trial labs before allowing them to enter the physical market. This experimentation is the key to creating a healthy proposition for machine learning to become a part of renewable energy.

At the University of Central Florida, a number of researchers are focusing on perovskite solar panels. Featuring a combination of inorganic and organic factors, this set has seen up to 28 per cent in increased efficiency during trial runs. This figure exceeds traditional silicon efficiencies and is quickly developing, so the opportunities already have experts raising their hopes.

Recently, solar panels have decreased in price, mostly because of economic factors that allow for cheaper manufacturing, construction and distribution. Adding on increased efficiency is a the cherry on top for consumers still sitting on the fence.

Creating better form

Meanwhile, the New York University, Stanford University and some members from NREL (based in Colorado) are setting their sights on using machine learning to create thin-film, organic solar panels. Although they are less efficient than traditional panels and the aforementioned perovskite combinations, they still carry a heavy advantage: they are more likely to generate greater amounts of electricity than other types. This is exactly why the material has been used in window pane technology, PV in consumer devices and a range of other applications.

These models still have room for improvement, ubt machine learning will help researchers to create chemistries that offer far more efficiency, and the ability to manufacture them at a cheaper price. While all of this is in its infancy, there’s a larger goal of relaying these benefits to consumers – something both the industry, business owners and homeowners can look forward to for the future.

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