高精度太阳能预测让工商业光伏储能系统更赚钱
Data-driven solar forecasting enables near-optimal economic decisions
- 用数据驱动模型预测未来7天每10分钟的太阳辐射
- 使工业用户投资回报率最高达12%，25年回本测试中提升50%
- 适合想降本增效的工商业能源决策者
太阳能应用对实现净零排放至关重要，但许多工业与商业主体难以判断是否应部署分布式光伏-储能系统，主要因缺乏快速、低成本且高分辨率的辐照度预报。本文提出SunCastNet，一种轻量级数据驱动预测系统，可提供0.05°空间分辨率、10分钟时间分辨率、最长7天的表面太阳辐射向下（SSRD）预测。结合强化学习（RL）进行电池调度，相比稳健决策方法（RDM），运营遗憾降低76%至93%。在25年投资回测中，该系统使每个区域高达五家高排放工业部门达到12%内部收益率（IRR）的商业可行性门槛。结果表明，高分辨率、长周期太阳能预测能直接转化为可观经济收益，支持近优能源运营并加速可再生能源部署。
原文摘要 · Abstract (English)
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.
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