Summary
This research introduces Physics-Informed State Space Models to improve solar irradiance forecasting for off-grid photovoltaic systems. It addresses critical anomalies in current deep learning models, such as temporal phase lags and physically impossible nocturnal power generation. By integrating atmospheric thermodynamics with data-driven modeling, this new approach aims to provide more reliable and stable energy predictions for autonomous systems.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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