Nou Julien, Chauvin Rémi, Eynard Julien, Thil Stéphane, Grieu Stéphane
PROMES-CNRS (UPR 8521), Rambla de la thermodynamique, Tecnosud, 66100 Perpignan, France.
Université de Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France.
Heliyon. 2018 Apr 16;4(4):e00598. doi: 10.1016/j.heliyon.2018.e00598. eCollection 2018 Apr.
Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon [Formula: see text] is up to [Formula: see text] ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models - either a persistence of DNI or a persistence of the clear-sky index - are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about [Formula: see text], with [Formula: see text], and [Formula: see text], with [Formula: see text].
通过优化运行提高发电厂效率是聚光太阳能发电（CSP）技术发展的关键。为此，最具挑战性的课题之一仍是对短期太阳能资源进行准确预测。事实上，在CSP发电厂中，发电量直接受到太阳能资源的可用性和变异性的影响，更具体地说，受到直射法向辐照度（DNI）的影响。本文探讨了一种新的DNI小时内预测方法（预测范围提前至 小时），利用了该量可分解为两个项的事实，即晴空DNI和晴空指数。晴空DNI通过DNI测量值进行预测，使用经验模型（Ineichen和Perez，2002年）并结合大气浑浊度的持续性。此外，在CSPIMP（聚光太阳能发电厂效率提升）研究项目框架内，法国国家科学研究中心太阳能热发电实验室（PROMES-CNRS）开发了一种能够提供高动态范围（HDR）图像的天空成像仪。因此，关于晴空指数，它是根据天空成像数据，使用基于自适应网络的模糊推理系统（ANFIS）进行预测的。已开发出一种混合算法，在晴空各向异性已知时借鉴了Ghonima等人（2012年）提出的分类算法，在相反情况下借鉴了Li等人（2011年）提出的混合阈值算法来检测云层。通过一项比较研究对性能进行评估，其中包括持续性模型——DNI的持续性或晴空指数的持续性。初步结果表明，所提出的方法在预测准确性方面有可能优于这些模型（两种持续性模型表现相似）：在所使用的测试数据上，均方根误差（RMSE）降低了约 ， 时为 ， 时为 。
