Multi-horizon solar forecasting technique cuts prediction errors across all time steps

Solar photovoltaic capacity hit a record 597 GW in 2024, making reliable power forecasting critical for grid stability. Existing deep‑learning models typically perform single‑horizon (point) prediction using ground‑based sky images (GSI), which fails to capture long‑term temporal dependencies. In a new arXiv preprint, researchers Sumit Laha, Ankit Sharma, and Hassan Foroosh demonstrate that switching from single‑horizon to multi‑horizon forecasting provides an architecture‑independent accuracy boost.

Their method jointly optimizes predictions over multiple discrete future time steps simultaneously. This forces the network to learn latent dependencies between successive outputs, preventing the weight gradients and filter diversity from converging prematurely. The technique works across diverse deep‑learning architectures, requiring no additional model complexity or parameter tuning. It leverages sequential sky imagery combined with historical PV generation data.

The authors report superior predictive accuracy and robustness across the entire forecast horizon. Because the multi‑horizon optimization imposes only negligible computational overhead compared to single‑horizon models, the approach is both scalable and efficient. This work provides a practical path to improve power grid resilience without heavy infrastructure upgrades, especially as solar penetration continues to rise.