PDF(Pubmed)
Abstract：
Today\'s many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Ağrı) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Ağrı province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.
摘要：
今天的许多巨大的行业，包括能源，工业,旅游,农业应密切跟踪太阳辐射的变化趋势，以从太阳中获得更多利益。然而,太阳辐射测量站的稀缺是一个重大障碍。这促使人们使用现有的气候和大气参数对各个地区的全球太阳辐射（GSR）进行估算。虽然预测方法不能取代直接测量的精度，它们对于在全球范围内研究和利用太阳能是无价的。从这个角度来看,本文重点预测了三个省(Afyonkarahisar，Rize,和Aörº）在图尔基耶表现出不同的太阳辐射分布。在这种情况下，基于梯度的优化器(GBO)，哈里斯·霍克斯优化(HHO)，藤壶交配优化器（BMO），正弦余弦算法(SCA)和亨利气体溶解度优化(HGSO)已用于对每日GSR数据进行建模。算法用包括日照时间在内的五个输入变量的每日历史数据进行了校准，实际压力,水分,风速,2010年至2017年之间的环境温度。然后，他们用2018年的每日数据进行了测试.在研究中,一系列统计指标(R2、MABE、RMSE,和MBE）被用来阐明以更高的精度预测太阳辐射数据的算法。预测结果表明，所有算法在Rize省都达到了最高的R2值。已经发现，Afyonkarahisar省的SCA（MABE为0.7023MJ/m2，RMSE为0.9121MJ/m2，MBE为0.2430MJ/m2）和GBO（RMSE为0.8432MJ/m2，MABE为0.6703MJ/m2，R2为0.8810）是估算GSR省数据的最有效算法。研究结果表明，本文测试的每种元启发式算法都有可能在令人满意的误差范围内预测每日GSR数据。然而,GBO和SCA算法为每日GSR数据提供了最准确的预测。
