MATNet introduces a hybrid approach that combines AI models with physical knowledge of PV generation. The model outperforms current methods, showcasing potential in improving forecasting accuracy and facilitating the integration of PV energy into the power grid. By leveraging multiple input sources, MATNet demonstrates resilience and reliability even under challenging weather conditions.
The content discusses the importance of accurate forecasting for renewable energy integration, focusing on photovoltaic (PV) units. It compares physics-based and data-based strategies, highlighting the limitations and advantages of each approach. The proposed MATNet architecture is detailed, explaining its components such as embedding module, positional encoding, self-attention mechanism, dense interpolation layer, and multi-level joint fusion.
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by Matteo Torto... às arxiv.org 03-05-2024
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