Data-driven solar forecasting enables near-optimal economic decisions

2025-09-08

AI Summary

To address the challenge faced by industrial and commercial users—namely, the lack of high-resolution, low-cost solar irradiance forecasts hindering investment decisions for distributed photovoltaic–battery storage systems—this paper proposes SunCastNet, a lightweight, data-driven model enabling high-accuracy surface solar radiation forecasting at 0.05° spatial resolution, 10-minute temporal granularity, and a 7-day horizon. Crucially, we pioneer the deep integration of this forecasting model with a reinforcement learning–based battery dispatch policy, embedded within a long-term energy-economic decision-making framework. Compared to conventional robust optimization approaches, our method reduces operational regret by 76%–93%. A 25-year backtesting analysis demonstrates that multiple high-emission industries achieve internal rates of return exceeding 12% under coordinated PV–storage investment—surpassing the critical commercial viability threshold and substantially enhancing the economic feasibility of renewable energy projects.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Solar energy adoption is critical to achieving net-zero emissions. However, it remains difficult for many industrial and commercial actors to decide on whether they should adopt distributed solar-battery systems, which is largely due to the unavailability of fast, low-cost, and high-resolution irradiance forecasts. Here, we present SunCastNet, a lightweight data-driven forecasting system that provides 0.05$^circ$, 10-minute resolution predictions of surface solar radiation downwards (SSRD) up to 7 days ahead. SunCastNet, coupled with reinforcement learning (RL) for battery scheduling, reduces operational regret by 76--93% compared to robust decision making (RDM). In 25-year investment backtests, it enables up to five of ten high-emitting industrial sectors per region to cross the commercial viability threshold of 12% Internal Rate of Return (IRR). These results show that high-resolution, long-horizon solar forecasts can directly translate into measurable economic gains, supporting near-optimal energy operations and accelerating renewable deployment.

Problem

• Provides high-resolution solar irradiance forecasts
• Enables optimal solar-battery system adoption decisions
• Reduces operational regret through reinforcement learning scheduling

Innovation

• Lightweight data-driven solar forecasting system
• High-resolution long-horizon SSRD predictions
• Reinforcement learning for battery scheduling optimization