Diffusion-driven adaptive radiance refinement with PPO-based optimization for robust solar irradiance forecasting

doi:10.1016/j.rineng.2026.109718

Diffusion-driven adaptive radiance refinement with PPO-based optimization for robust solar irradiance forecasting

2026-02-23

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Natrayan Lakshmaiya *

C

Chenga Reddy Peddamangari

DOI：10.1016/j.rineng.2026.109718

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• Introduces a novel fusion of diffusion-based refinement and reinforcement learning to enhance solar irradiance forecasting and PV control. • PPO-based controller continuously self-corrects prediction errors in real time, ensuring adaptive and resilient PV system performance. • Achieves around 8 % improvement in prediction accuracy and notable reduction in forecast volatility compared to advanced deep learning and hybrid models. • Delivers a scalable, real-time, and autonomous forecasting-and-control framework suitable for smart grid and distributed solar energy applications.

Keywords:

Solar irradiance forecasting

Diffusion-based radiance refinement

Proximal policy optimization

Deep learning for Renewable energy

Sustainable energy systems

Diffusion-driven adaptive radiance refinement with PPO-based optimization for robust solar irradiance forecasting

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