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A new approach to the real-time assessment and intraday forecasting of clear-sky direct normal irradiance

机译：晴空直接法向辐照度实时评估和日内预报的新方法

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Clear-sky Direct Normal Irradiance (DNI) is the solar power received at ground level per unit of area, at a specific location, under cloud-free conditions. Regarding Concentrating Solar Power (CSP) technologies, such conditions mean that there is no cloud between the Sun and the observer, i.e. the solar field. Since clear sky defines the nominal operating conditions of CSP plants, real-time estimates and forecasts of clear-sky DNI are key information for power plant operators tasked with the management of those plants. So, the present paper focuses first on a new algorithm for the real-time detection of clear-sky situations from DNI measurements. This algorithm makes use of the last-known clear-sky situation and requires the maximum speed at which the atmosphere becomes opaque to be evaluated. The paper also focuses on an efficient approach to the real-time assessment of clear-sky DNI. This approach combines the model developed by Ineichen and Perez with a persistence of atmospheric turbidity, taking advantage of the fact that changes in this quantity are relatively small throughout the day in comparison to changes in DNI, even when the sky is free of clouds. Performance is evaluated via a comparative study, in which empirical models are included, using one-minute data from two sites (Golden, USA, and Perpignan, France). MAE and RMSE are lower than 10 W m(-2) and 21 W m(-2), respectively. The same approach is capable of providing accurate intraday forecasts of clear-sky DNI. It has proven to be the best compromise between accuracy and complexity (reference is a persistence of DNI) among the considered approaches, including neuro-fuzzy approaches. For a forecast horizon of 5 h,MAE similar or equal to 30 W m(-2) and RMSE similar or equal to 37 W m(-2).
机译：晴空直接法向辐照度（DNI）是在无云条件下在特定位置在特定位置的单位面积在地面上接收的太阳能。关于聚光太阳能（CSP）技术，这种情况意味着在太阳和观察者之间，即太阳场之间没有云。由于晴空定义了CSP电厂的正常运行条件，因此晴空DNI的实时估计和预测是负责这些电厂管理的电厂运营商的关键信息。因此，本文首先着重于从DNI测量值实时检测晴空情况的新算法。该算法利用了最后已知的晴朗天空情况，并且需要对大气变得不透明的最大速度进行评估。本文还着重介绍了一种有效的方法来实时评估晴空DNI。这种方法将Ineichen和Perez开发的模型与持续的大气浑浊相结合，利用了这样一个事实，即与DNI的变化相比，即使在天空没有云的情况下，该数量的变化在一天当中相对较小。通过一项比较研究对绩效进行评估，该研究包括来自两个地点（美国戈尔登和法国佩皮尼昂）的一分钟数据，其中包括经验模型。 MAE和RMSE分别低于10 W m（-2）和21 W m（-2）。同样的方法能够提供晴朗的DNI的准确日内预测。在考虑的方法（包括神经模糊方法）中，这已被证明是准确性和复杂性之间的最佳折衷（参考是DNI的持久性）。对于5小时的预测范围，MAE等于或等于30 W m（-2），RMSE等于或等于37 W m（-2）。

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