IMPACT: Inverter-Informed Multimodal Learning for Photovoltaic Generation Forecasting
This work develops a physics-informed multimodal deep learning framework for 10- to 110-minute-ahead probabilistic photovoltaic power forecasting. The forecasting model is built on a four-branch architecture that separately encodes plant-level temporal history and inverter-level spatial cloud dynamics through a graph a...
Anushka Katiyar, Cong Feng, Jing Wang et al.
· 0 citations