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

2026-05-18

AI Summary

This study addresses the grid instability caused by the intermittent nature of solar irradiance in photovoltaic power generation. Existing approaches are often limited to single-step forecasting and tied to specific model architectures. To overcome these limitations, this work proposes an architecture-agnostic, multi-step joint prediction framework that simultaneously optimizes outputs across multiple future time steps, thereby enhancing the model’s capacity to capture long-term temporal dependencies. By integrating sequential sky images with historical power generation data, the framework is readily adaptable to diverse deep learning architectures. It achieves substantial improvements in prediction accuracy and robustness across all time horizons with negligible additional computational overhead, effectively strengthening grid resilience.

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.

Problem

• photovoltaic power forecasting
• multi-horizon prediction
• temporal dependencies
• solar irradiance intermittency
• grid stability

Innovation

• multi-horizon forecasting
• temporal dependencies
• photovoltaic power prediction
• deep learning
• architecture-independent