A truly sustainable future requires solar power, but trying to consistently maximize the energy harvested by panel arrays remains one of the industry’s biggest challenges. Unlike fossil fuels, solar power yields are dictated by the complex interplay of weather and atmospheric variables, as well as the sun’s own activity. This means it’s basically impossible to craft a universal prediction model, so localized solar forecast systems are a necessity.
While machine learning technology has significantly improved today’s forecast models, there is still a lot of room for improvement. But an artificial intelligence program is only as good as the data used to train it—and according to researchers at Australia’s Charles Darwin University, it’s tough to find a…
