Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting out of Brussels, Belgium, by NewsRx edit ors, research stated, "Dynamic Thermal Rating (DTR) enhances grid flexibility by adapting line capabilities to weather conditions. For this purpose, DTR-based t echnologies require reliable and continuous measurement of the conductor tempera ture along the line route, which could hinder their wide -scale deployment due t o the prohibitively high number of required sensors." Financial supporters for this research include National Science Foundation, Neth erlands, Service Public de Wallonie, Belgium Recherche, Fonds de la Recherche Sc ientifique - FNRS, Walloon Region, Belgium. Our news journalists obtained a quote from the research from the University libr e of Bruxelles, "Existing machine learning -based DTR methods infer conductor te mperature from weather variables avoiding using complex and expensive measuremen t techniques, but their estimation accuracy greatly relies on the availability o f a comprehensive set of measured data. To face these issues, this paper propose s the usage of transfer learning, a data -driven technique allowing the reductio n of the number of sensors by transferring knowledge from a single calibrated so urce sensor to many target sensors. To the best of the author's knowledge, at th e time of writing, the proposed approach is the first application of Transfer Le arning in the domain of DTR which is validated on real transmission lines data."
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