用工程化天空图像特征提升太阳能辐照度预测精度
Deep Learning Multi-Horizon Irradiance Nowcasting: A Comparative Evaluation of Three Methods for Leveraging Sky Images
- 将天空图像转为云层、运动等人工特征后聚合输入模型
- 15分钟内预测误差低于基准方法，技能得分提升显著
- 适合光伏调度与电力系统研究者参考
本文对比三种将全天空成像仪（ASI）图像融入深度学习辐照度短时预报的方法。第一种直接用卷积神经网络（CNN）处理原始RGB图像；第二种基于领域知识（如云分割、云移动矢量、太阳位置、云底高度）生成二维特征图，再输入CNN提取复合特征；第三种将这些工程化特征按时间序列聚合后输入模型。所有方法均在高频、29天的训练数据上训练，实现最多15分钟的多时域全球水平辐照度预报。评估结果显示，使用聚合工程特征作为输入的模型表现最优，在7个测试日上的均方根误差和技能得分均优于其他方法。结果表明，无需复杂空间有序的深度学习架构，也能有效融合天空图像信息，为替代性图像处理方法及更优的空间特征学习提供了可能。
原文摘要 · Abstract (English)
We investigate three distinct methods of incorporating all-sky imager (ASI) images into deep learning (DL) irradiance nowcasting. The first method relies on a convolutional neural network (CNN) to extract features directly from raw RGB images. The second method uses state-of-the-art algorithms to engineer 2D feature maps informed by domain knowledge, e.g., cloud segmentation, the cloud motion vector, solar position, and cloud base height. These feature maps are then passed to a CNN to extract compound features. The final method relies on aggregating the engineered 2D feature maps into time-series input. Each of the three methods were then used as part of a DL model trained on a high-frequency, 29-day dataset to generate multi-horizon forecasts of global horizontal irradiance up to 15 minutes ahead. The models were then evaluated using root mean squared error and skill score on 7 selected days of data. Aggregated engineered ASI features as model input yielded superior forecasting performance, demonstrating that integration of ASI images into DL nowcasting models is possible without complex spatially-ordered DL-architectures and inputs, underscoring opportunities for alternative image processing methods as well as the potential for improved spatial DL feature processing methods.
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