Data-driven solar forecasting enables near-optimal economic decisions
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916940081004544 |
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| author | Dai, Zhixiang Yin, Minghao Chen, Xuanhong Carpentieri, Alberto Leinonen, Jussi Bonev, Boris Zhong, Chengzhe Kurth, Thorsten Sun, Jingan Cherukuri, Ram Zhang, Yuzhou Zhang, Ruihua Hariri, Farah Ding, Xiaodong Zhu, Chuanxiang Zhang, Dake Cui, Yaodan Lu, Yuxi Song, Yue He, Bin Chen, Jie Zhu, Yixin Xu, Chenheng Liu, Maofeng Niu, Zeyi Qi, Wanpeng Shan, Xu Xian, Siyuan Lin, Ning Feng, Kairui |
| author_facet | Dai, Zhixiang Yin, Minghao Chen, Xuanhong Carpentieri, Alberto Leinonen, Jussi Bonev, Boris Zhong, Chengzhe Kurth, Thorsten Sun, Jingan Cherukuri, Ram Zhang, Yuzhou Zhang, Ruihua Hariri, Farah Ding, Xiaodong Zhu, Chuanxiang Zhang, Dake Cui, Yaodan Lu, Yuxi Song, Yue He, Bin Chen, Jie Zhu, Yixin Xu, Chenheng Liu, Maofeng Niu, Zeyi Qi, Wanpeng Shan, Xu Xian, Siyuan Lin, Ning Feng, Kairui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06925 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven solar forecasting enables near-optimal economic decisions Dai, Zhixiang Yin, Minghao Chen, Xuanhong Carpentieri, Alberto Leinonen, Jussi Bonev, Boris Zhong, Chengzhe Kurth, Thorsten Sun, Jingan Cherukuri, Ram Zhang, Yuzhou Zhang, Ruihua Hariri, Farah Ding, Xiaodong Zhu, Chuanxiang Zhang, Dake Cui, Yaodan Lu, Yuxi Song, Yue He, Bin Chen, Jie Zhu, Yixin Xu, Chenheng Liu, Maofeng Niu, Zeyi Qi, Wanpeng Shan, Xu Xian, Siyuan Lin, Ning Feng, Kairui Geophysics Machine Learning 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. |
| title | Data-driven solar forecasting enables near-optimal economic decisions |
| topic | Geophysics Machine Learning |
| url | https://arxiv.org/abs/2509.06925 |