Multi-Horizon Forecasting Improves Solar PV Output Predictions
A new paper on arXiv proposes a shift from single-horizon to multi-horizon forecasting for photovoltaic power output prediction. The authors argue that joint optimization over a sequence of future values helps deep neural networks capture latent inter-step temporal dependencies, avoiding premature convergence. The study highlights that global solar PV capacity reached a record 597 GW in 2024, underscoring the need for robust forecasting to mitigate grid instability from intermittent solar irradiance. The paper criticizes existing literature for being constrained by single-architecture evaluations and exclusive focus on point predictions. The proposed framework claims architecture-independent accuracy improvements.
Key facts
- arXiv paper 2605.19074 proposes multi-horizon forecasting for PV power output.
- Global solar PV capacity reached a record 597 GW in 2024.
- The paper criticizes existing approaches for single-architecture evaluations and single-horizon prediction.
- Joint optimization over future values helps capture inter-step temporal dependencies.
- The framework claims architecture-independent accuracy improvements.
- The study uses ground-based sky images (GSI) for deep learning-based forecasting.
- The paper is categorized as a cross-type announcement on arXiv.
- The approach aims to mitigate grid instability from intermittent solar irradiance.
Entities
Institutions
- arXiv