A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors
A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors
复制标题
利用云运动矢量进行卫星概率太阳预报的新方法
作者：
Thomas Carrière;R. Amaro e Silva;Fuqiang Zhuang;Y. Saint;P. Blanc
概率太阳能预测是一个越来越重要的问题，光伏（PV）能源的整合。然而，对于短期应用，估计预测的不确定性是具有挑战性的，通常委托给统计模型。为了解决这个问题，目前的工作提出了一种方法，结合物理和统计基础，并利用卫星派生的晴空指数（KC）和云运动矢量（CMV），传统上用于确定性预测。预测的不确定性估计通过使用CMV在一个不同的方式比通常使用的标准CMV为基础的预测方法，并通过实施一个集成方法的基础上的高斯噪声添加步骤的kc和CMV估计。使用15分钟的平均地面测量的全球水平辐照度（GHI）数据在法国的两个位置作为参考，该模型显示，大大超过基线概率预测完整的历史持久性增强（CH-PeEn），减少连续排名概率得分（CRPS）之间的37%和62%，这取决于预测范围。结果还表明，这主要是通过提高模型的清晰度来驱动的，这是使用预测区间归一化平均宽度（PINAW）度量来衡量的。
Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.