A novel daily and 15-min period solar resource classification algorithm, based only on the Fourier transform of global horizontal irradiance (GHI) and calculable parameters, is presented. These methods can identify GHI series that are not attenuated by clouds, whether the clouds may or may not be visible. Additional sky condition classes are also adopted in which the representative conditions of the remaining days or periods are classified. Although the proposed day classification is too conservative to be widely applicable, the 15-min period classification is generally in good agreement with the Perez sky clearness index while providing greater sensitivity to turbid conditions and algorithm flexibility. It is shown, however, that the adoption of the clearness index in the proposed 15-min period method less successfully distinguished between clear and constant overcast conditions under winter continental conditions due to relatively large solar zenith angles. It is also shown that the proposed 15-min period and Reno and Hansen statistical methods are generally in good agreement on the detection of clear conditions under temperate conditions. The classification results of the proposed method are further shown in comparison to high-resolution sky images to elucidate the character of each class and the classification algorithm flexibility.

中文翻译：

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基于频域分析的全球水平辐照度时间序列太阳能资源分类算法

提出了一种新的每日和 15 分钟周期太阳能资源分类算法，该算法仅基于全球水平辐照度 (GHI) 和可计算参数的傅立叶变换。这些方法可以识别没有被云衰减的 GHI 系列，无论云是否可见。还采用了额外的天空条件类，其中对剩余天数或周期的代表性条件进行了分类。尽管建议的日分类过于保守而不能广泛适用，但 15 分钟周期分类通常与Perez 天空晴朗指数非常吻合，同时对混浊条件和算法灵活性提供更大的敏感性。然而，结果表明，采用清晰指数在提议的 15 分钟周期方法中，由于太阳天顶角相对较大，因此在冬季大陆条件下区分晴朗和恒定阴天条件不太成功。还表明，建议的 15 分钟周期以及Reno 和 Hansen统计方法在温带条件下检测晴朗条件方面通常具有良好的一致性。将所提方法的分类结果与高分辨率天空图像进行了比较，以阐明每个类别的特征和分类算法的灵活性。