Data-driven solar forecasting enables near-optimal economic decisions
This addresses the challenge for industrial and commercial actors in making economic decisions about solar energy adoption, representing a strong specific gain rather than a broad breakthrough.
The paper tackles the problem of high-resolution, long-horizon solar forecasting for distributed solar-battery systems, presenting SunCastNet, which reduces operational regret by 76-93% and enables up to five of ten high-emitting industrial sectors per region to achieve commercial viability in investment backtests.
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.