HAL (France)
open access
Improving forecasting accuracy of renewable energy generation via periodicity-aware deep learning framework with Time2Vec
Accurate forecasting of photovoltaic (PV) and wind power generation is vital for reliable renewable energy operations. However, their differing temporal characteristics, especially in periodicity, pose challenges for unified prediction models. This paper proposes a periodicity-aware hybrid deep lear
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