用超分辨率模型提升气候数据精度，更准预测风能光伏未来发电量。
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model
- 基于循环扩散机制融合预训练解码器，生成高分辨率气候数据。
- 在内蒙古额济纳地区验证，对风电光伏长期发电量预测误差显著降低。
- 适合能源规划者与气候影响研究者，解决低分辨率数据带来的偏差问题。
受全球气候变化和能源转型推动，电力供应能力与气象因素的耦合日益重要。长期准确量化气候变化对可再生能源发电的影响，对可持续电力系统发展至关重要。然而，由于跨学科数据需求差异，气候数据常缺乏必要的小时级分辨率，难以捕捉可再生能源资源的短期波动与不确定性。为此，本文提出超分辨率循环扩散模型（SRDM），通过预训练解码器与去噪网络结合，利用循环耦合机制生成长期高分辨率气候数据。该数据经机理模型转换为电力值，实现对未来长期尺度下风能与光伏（PV）发电的模拟。以中国内蒙古额济纳地区为例，使用第五代再分析数据（ERA5）和耦合模型比较计划数据（CMIP6），在两种气候路径（SSP126与SSP585）下开展案例研究。结果表明，SRDM在生成超分辨率气候数据方面优于现有生成模型；同时揭示了使用低分辨率气候数据进行功率转换时引入的显著估计偏差。
原文摘要 · Abstract (English)
Driven by global climate change and the ongoing energy transition, the coupling between power supply capabilities and meteorological factors has become increasingly significant. Over the long term, accurately quantifying the power generation of renewable energy under the influence of climate change is essential for the development of sustainable power systems. However, due to interdisciplinary differences in data requirements, climate data often lacks the necessary hourly resolution to capture the short-term variability and uncertainties of renewable energy resources. To address this limitation, a super-resolution recurrent diffusion model (SRDM) has been developed to enhance the temporal resolution of climate data and model the short-term uncertainty. The SRDM incorporates a pre-trained decoder and a denoising network, that generates long-term, high-resolution climate data through a recurrent coupling mechanism. The high-resolution climate data is then converted into power value using the mechanism model, enabling the simulation of wind and photovoltaic (PV) power generation on future long-term scales. Case studies were conducted in the Ejina region of Inner Mongolia, China, using fifth-generation reanalysis (ERA5) and coupled model intercomparison project (CMIP6) data under two climate pathways: SSP126 and SSP585. The results demonstrate that the SRDM outperforms existing generative models in generating super-resolution climate data. Furthermore, the research highlights the estimation biases introduced when low-resolution climate data is used for power conversion.
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