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首页> 外文期刊>International Journal of Mathematical Analysis and Applications >Improved Mathematical Modeling of the Hourly Solar Diffuse Fraction (HSDF) - Adrar, Algeria Case Study
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Improved Mathematical Modeling of the Hourly Solar Diffuse Fraction (HSDF) - Adrar, Algeria Case Study

机译:每小时太阳扩散分数(HSDF)的改进数学建模-阿尔及利亚阿德拉尔案例研究

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摘要

Solar energy is among the excellent alternative energy resources; however, it suffers from significant problems. These problems are mainly due to the inherent variability, and intermittency of the solar resource; however, proper predictability of the resource can reduce the consequent impacts of the mentioned problems. Enhancing the predictability of the solar resource provides an essential tool for the design, performance analysis, and economic evaluation of various solar energy projects. In this paper, highly accurate mathematical models for estimating the hourly diffuse solar fraction are presented for enhancing the predictability of the solar resource over Adrar, Algeria big south desert. The presented modeling is based on clearance index measurements. The best found model for the considered site is found to be the sigmoid logistic empirical model. This model shows the highest accuracy in comparison with other models where its correlation coefficient (R), and the Nash-Sutcliffe NSE are found to be 93.7% and 84.2% respectively. In addition, the segmoid logistic model shows very low values of the mean bias error (MBE), and root mean square error (RMSE).
机译:太阳能是极好的替代能源之一。但是,它存在重大问题。这些问题主要是由于固有的可变性和太阳能资源的间歇性造成的。但是,正确的资源可预测性可以减少上述问题的后果。增强太阳能的可预测性为各种太阳能项目的设计,性能分析和经济评估提供了必不可少的工具。在本文中,提出了用于估计小时扩散太阳分数的高精度数学模型,以增强阿尔及利亚大南部沙漠阿德拉尔上太阳能资源的可预测性。提出的建模基于间隙指数测量。对于所考虑的站点,发现的最佳模型是S型logistic经验模型。与其他模型的相关系数(R)和Nash-Sutcliffe NSE分别为93.7%和84.2%相比,该模型显示出最高的准确性。此外,Segmoid Logistic模型显示的均值偏差误差(MBE)和均方根误差(RMSE)值非常低。

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