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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Spatiotemporal Pattern Recognition and Nonlinear PCA for Global Horizontal Irradiance Forecasting
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Spatiotemporal Pattern Recognition and Nonlinear PCA for Global Horizontal Irradiance Forecasting

机译：时空模式识别和非线性PCA的全球水平辐照度预测

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摘要

This letter presents a novel technique for the forecast of the ground horizontal irradiance (GHI) from satellite-based images. To enhance the forecast accuracy, spatial information in addition to temporal information has been considered. This produced an increase in the computational load of the forecast process. Dimensionality reduction techniques based on nonlinear principal component analysis (PCA) are used to project the original data set into low-dimension feature space. A multilayer feedforward neural network classifier is used to model the signal through a training operation involving past history of the considered spatiotemporal signal. Experiments have been carried out on two different data sets. Comparisons with classical forecasting techniques demonstrate that the introduction of the spatial information permits to obtain better short-term forecast measurements for all types of sky conditions. Moreover, further analysis demonstrates that, compared with linear PCA, the nonlinear PCA is more appropriate for dimensionality reduction of spatiotemporal GHI data set.
机译：这封信提出了一种新技术，用于从基于卫星的图像中预测地面水平辐照度（GHI）。为了提高预测准确性，已经考虑了除了时间信息之外的空间信息。这增加了预测过程的计算量。基于非线性主成分分析（PCA）的降维技术用于将原始数据集投影到低维特征空间中。多层前馈神经网络分类器用于通过训练操作对信号进行建模，该训练操作涉及所考虑的时空信号的过去历史。已经在两个不同的数据集上进行了实验。与经典预测技术的比较表明，空间信息的引入允许针对所有类型的天空条件获得更好的短期预测测量结果。此外，进一步的分析表明，与线性PCA相比，非线性PCA更适用于时空GHI数据集的降维。

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