Data-driven solar forecasting enables near-optimal economic decisions
Zhixiang Dai, Minghao Yin, Xuanhong Chen, Alberto Carpentieri, Jussi Leinonen, Boris Bonev, Chengzhe Zhong, Thorsten Kurth, Jingan Sun, Ram Cherukuri, Yuzhou Zhang, Ruihua Zhang, Farah Hariri, Xiaodong Ding, Chuanxiang Zhu, Dake Zhang, Yaodan Cui, Yuxi Lu, Yue Song, Bin He, Jie Chen, Yixin Zhu, Chenheng Xu, Maofeng Liu, Zeyi Niu, Wanpeng Qi, Xu Shan, Siyuan Xian, Ning Lin, Kairui Feng
Published: 2025/9/8
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.