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Researchers from Canada's University of Waterloo have developed an innovative model that utilizes deep learning and image processing to detect snow coverage on photovoltaic (PV) panels. This automated system can estimate energy loss due to snow accumulation, which can reach up to 34% in cold climates. The model outperformed traditional image processing methods, showcasing its potential for maximizing energy output and maintaining solar panel integrity.
The method involves a five-step process, including image dataset creation, segmentation using a convolutional neural network, and energy loss estimation based on snow coverage. The model demonstrated a mean error of just 2.88% in snow identification and effectively predicted energy losses with high accuracy. This advancement not only aids in optimizing solar energy production but also highlights the growing role of AI in renewable energy technologies.
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Isomorphic Labs, the AI drug discovery platform that was spun out of Google's DeepMind in 2021, has raised external capital for the first time. The $600
How to level up your teaching with AI. Discover how to use clones and GPTs in your classroom—personalized AI teaching is the future.
Trump's Third Term? AI already knows how this can be done. A study shows how OpenAI, Grok, DeepSeek & Google outline ways to dismantle U.S. democracy.
Sam Altman today revealed that OpenAI will release an open weight artificial intelligence model in the coming months. "We are excited to release a powerful new open-weight language model with reasoning in the coming months," Altman wrote on X.
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