<p class="jove_content">This article presents a cross-site physics-informed transfer learning framework for one-hour-ahead photovoltaic power forecasting that combines transfer learning, plant-level descriptors, and physics-informed constraints across heterogeneous solar plants.</p>

cross-site-physics-informed-transfer-learning-for-one-hour-ahead

Cross-Site Physics-Informed Transfer Learning for One-Hour-Ahead Photovoltaic Power Forecasting

Accurate short-term photovoltaic power forecasting is essential for grid scheduling, reserve allocation, and the reliable integration of renewable energy ...

<p class="jove_content">HMP-MUNet introduces a hybrid deep learning framework for automated skin lesion segmentation. By integrating State-Space Models with multi-scale attention mechanisms, it enhances segmentation accuracy while optimizing computational efficiency, offering a robust solution for skin cancer diagnosis in clinical settings.</p>

hmp-munet-hybrid-deep-learning-framework-for-automated-skin-lesion

Institution - Shenyang University of Chemical Technology

Articles Published in JoVE