用基础模型实现零样本太阳能辐照预测，新电站无需历史数据也能准度超群。
SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models
- 基于基础模型做零样本迁移，不依赖本地历史数据
- 在无历史数据场景下性能比顶尖模型提升约70%
- 适合新建光伏电站快速部署，推动可再生能源接入电网
传统太阳能预测模型通常依赖长达五年或以上的站点特定历史辐照数据，而这些数据对新建光伏电站往往不可得。由于可再生能源具有高度间歇性，构建精准的太阳能辐照预测系统对高效电网管理及持续推广太阳能至关重要，是实现联合国净零目标的关键。本文提出SPIRIT，一种利用基础模型进行太阳能辐照预测的新方法，使其适用于新建成的太阳能设施。该方法在零样本迁移学习中性能较现有最优模型提升约70%，可在无任何历史数据的情况下实现有效预测。随着更多本地数据可用，通过微调可进一步提升性能。相关结果经统计显著性检验验证。SPIRIT代表了快速、可扩展且适应性强的太阳能预测解决方案的重要进展，助力可再生能源在全球电力系统中的融合。
原文摘要 · Abstract (English)
Traditional solar forecasting models are based on several years of site-specific historical irradiance data, often spanning five or more years, which are unavailable for newer photovoltaic farms. As renewable energy is highly intermittent, building accurate solar irradiance forecasting systems is essential for efficient grid management and enabling the ongoing proliferation of solar energy, which is crucial to achieve the United Nations' net zero goals. In this work, we propose SPIRIT, a novel approach leveraging foundation models for solar irradiance forecasting, making it applicable to newer solar installations. Our approach outperforms state-of-the-art models in zero-shot transfer learning by about 70%, enabling effective performance at new locations without relying on any historical data. Further improvements in performance are achieved through fine-tuning, as more location-specific data becomes available. These findings are supported by statistical significance, further validating our approach. SPIRIT represents a pivotal step towards rapid, scalable, and adaptable solar forecasting solutions, advancing the integration of renewable energy into global power systems.
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