The software tool uses self-supervised learning to detect long-term defects in solar assets weeks or years before ...

Solar power is an important source of renewable energy. Therefore, in several projects, Statistic Netherlands carries out research with which we can improve the estimate of the total amount of solar ...

Scientists need to put more effort and resources into developing deep-learning forecasting models that consider the morphology of PV panels, a group of academics led by the University of Cambridge ...

A group of investors including Statkraft Ventures has invested US$3 million in software-as-a-service (SaaS) company Glint Solar and its site identification technology for both ground mount and ...

Renewable energy is the future, but at present no one is tracking just who’s got solar panels on their roof, in their back yard, or a shared neighborhood installation. Fortunately, solar panels ...

Today IBM Research announced that solar and wind forecasts produced using machine learning and other cognitive computing technologies are proving to be as much as 30 percent more accurate than ones ...

The work was supported by Descartes Labs https://descarteslabs.com/, who provided the cloud computation infrastructure, and World Resources Institute, who provided ...

Deep learning has been used to identify 1.47 million solar installations across the United States, exceeding the latest estimate of 1.02 million. What’s new: Solar panels are becoming increasingly ...

Do you remember the excitement around the 2017 total solar eclipse? Well, it is going to happen again on Monday, April 8, of this year. In a path a little more than 100 miles across, going ...