用公共摄像头图像和时间序列预测日间光照，准确率提升超四分之一。
Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series
- 仅用单帧公共摄像头图像与归一化时间序列输入。
- 相比行业领先服务Solcast，预测误差降低25.95%。
- 轻量多模态模型适配多种摄像头，实用性强。
精确的日间太阳辐照度预测对优化电力调度与交易至关重要。本文提出一种新方法，包含三个创新点：1）首次使用单帧公共摄像头图像；2）对太阳辐照度时间序列采用新提出的归一化步骤，显著提升性能；3）设计轻量级多模态模型Solar Multimodal Transformer（SMT），融合图像与归一化时间序列，实现高精度短时预测。在基准测试中，本模型相较领先服务商Solcast，预测准确率提升25.95%。该方法可灵活适配不同摄像头规格，适用于多种真实场景下的太阳能预测需求。
原文摘要 · Abstract (English)
Accurate intraday solar irradiance forecasting is crucial for optimizing dispatch planning and electricity trading. For this purpose, we introduce a novel and effective approach that includes three distinguishing components from the literature: 1) the uncommon use of single-frame public camera imagery; 2) solar irradiance time series scaled with a proposed normalization step, which boosts performance; and 3) a lightweight multimodal model, called Solar Multimodal Transformer (SMT), that delivers accurate short-term solar irradiance forecasting by combining images and scaled time series. Benchmarking against Solcast, a leading solar forecasting service provider, our model improved prediction accuracy by 25.95%. Our approach allows for easy adaptation to various camera specifications, offering broad applicability for real-world solar forecasting challenges.
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