融合云图与气象数据，动态调整预测步长的太阳辐照度预测模型
Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting
- 用InceptionNeXt提取多尺度云图特征，捕捉复杂天气下的空间变化
- 提出步长自适应补偿模块，根据预测时间动态调整低频信息
- 在真实光伏站和NREL数据集上表现优于现有方法，适合短期电力调度
超短期太阳辐照度预测对光伏系统调度和电网稳定至关重要。现有方法存在三大缺陷：单一时间序列模型无法捕捉复杂条件下云层的空间动态，标准卷积难以表征多尺度云特征，固定低频补偿策略无法适应不同预测步长。为此，本文提出一种多源数据融合模型用于超短期辐照度预测。首先利用InceptionNeXt从地面云图中提取多尺度、多方向的空间特征；随后引入步长自适应低频补偿单元，根据预测步长动态调节全局低频信息；最后将增强后的图像特征与气象时间序列特征融合，通过TempAttnLSTM网络捕捉全局时序依赖关系，实现多步预测。在公开的NREL数据集及山东实际光伏电站上的实验表明，该方法优于多种前沿模型。
原文摘要 · Abstract (English)
Ultra-short-term solar irradiance prediction is critical for photovoltaic system dispatch and power grid stability. Existing approaches suffer from three key shortcomings: single time-series models cannot capture the spatial dynamics of clouds under complex conditions, standard convolutions inadequately represent multi-scale cloud features, and fixed low-frequency compensation strategies fail to adapt to different prediction steps. To address these issues, this proposes a multi-source data fusion model for ultra-short-term irradiance prediction. The model first employs InceptionNeXt to extract multi-scale, multi-directional spatial features from ground-based cloud images. A step-adaptive low-frequency compensation unit is then introduced to dynamically modulate global low-frequency information based on the prediction step. Eventually, the enhanced image features are combined with meteorological time-series features, and a TempAttnLSTM network captures global temporal dependencies for multi-step prediction. Experiments on the public NREL dataset and practical photovoltaic stations in Shandong illustrate the effectiveness of the proposed method compared with several state-of-the-art approaches.
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