Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting
arXiv:2606.06102v1 Announce Type: cross Abstract: Ultra-short-term solar irradiance prediction is critical for photovoltaic system dispatch and power grid stability. Existing approaches suffer from three key shortcomings: single time-series models cannot capture the spatial dynamics of clouds under complex conditions, standard convolutions inadequately represent multi-scale cloud features, and fixed low-frequency compensation strategies fail to adapt to different prediction steps. To address these issues, this proposes a multi-source data fusion model for ultra-short-term irradiance prediction
The increasing integration of renewable energy sources like solar into national grids is driving urgent demand for more accurate short-term forecasting to maintain stability and efficiency.
Improved ultra-short-term solar irradiance forecasting directly enhances the reliability and dispatchability of photovoltaic systems, critical for grid operators and energy policy makers.
This advancement provides a more robust and adaptive method for predicting solar energy output, potentially reducing reliance on traditional fossil fuel peaker plants and improving grid resilience.
- · Renewable energy operators
- · Smart grid technology providers
- · Energy storage companies
- · AI/ML research institutions
- · Conventional energy forecasting models
- · Fossil fuel peaker plant operators
More stable and predictable integration of solar power into electricity grids.
Accelerated investment in solar infrastructure due to reduced operational uncertainty and higher efficiency.
Potentially enables higher penetration of renewables on national grids, impacting the global energy mix and reducing carbon emissions.
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Read at arXiv cs.LG