2026-05-20 04:00 UTCOriginal source2 min readUpdated: 2026-06-30 13:03 UTC

Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting

This paper proposes an architecture-independent multi-horizon forecasting framework for photovoltaic power output prediction that jointly optimizes over future values, improving accuracy and robustness by capturing temporal dependencies and avoiding premature convergence.

SourcearXiv Computer VisionAuthor: Sumit Laha, Ankit Sharma, Hassan Foroosh

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Key points

Global solar PV capacity reached a record 597 GW in 2024, highlighting the need for robust forecasting to mitigate grid instability from intermittent irradiance.
Existing deep learning approaches often focus on single-horizon prediction and single architectures; this work introduces a multi-horizon framework that works across architectures.
Joint optimization over a sequence of future values helps deep neural networks better capture inter-step temporal dependencies by preventing precocious convergence.
The multi-horizon approach significantly enhances predictive accuracy and robustness across the forecast horizon with negligible computational overhead compared to single-horizon models.

Why it matters

This matters because global solar PV capacity reached a record 597 GW in 2024, highlighting the need for robust forecasting to mitigate grid instability from intermittent irradiance.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

This panel is AI-generated and reviewed for accuracy.

[2605.19074] Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting

[Submitted on 18 May 2026]

Title:Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting

View a PDF of the paper titled Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting, by Sumit Laha and 2 other authors

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Abstract:The rapid global expansion of solar photovoltaic (PV) capacity-reaching a record 597 GW in 2024-highlights the urgent need for robust forecasting models to mitigate the grid instability caused by the intermittent nature of solar irradiance. While deep learning-based direct forecasting using ground-based sky images (GSI) has emerged as a dominant approach, existing literature is often constrained by single-architecture evaluations and an exclusive focus on single-horizon (point) prediction. This paper proposes a transition from traditional single-horizon estimation toward a multi-horizon forecasting framework, leading to an architecture-independent improvement in accuracy. We hypothesize and demonstrate experimentally that joint optimization over a sequence of future values allows deep neural networks to better capture latent inter-step temporal dependencies by avoiding precocious convergence of the network in terms of both weight gradients and filter diversity. Leveraging this architecture-independent improvement that integrates sequential sky imagery with historical PV generation data, we evaluate the models' abilities to predict power output across multiple discrete future time steps simultaneously. Our methodology is validated through a comparative analysis across diverse deep learning architectures. The results demonstrate that this multi-horizon approach significantly enhances predictive accuracy and robustness across the entire forecast horizon while maintaining computational parsimony. By achieving superior performance with negligible overhead compared to single-horizon models, this work provides a scalable and efficient solution to improve the resilience of modern power grids.

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

Cite as: arXiv:2605.19074 [cs.CV]

(or arXiv:2605.19074v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.19074

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Sumit Laha [view email] [v1] Mon, 18 May 2026 19:56:34 UTC (934 KB)

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