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Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods

机译：基于机器学习方法的气象因素的区域最优结合改善中国太阳辐射估计

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摘要

The values of global solar radiation are important fundamental data for potential evapotranspiration estimation, solar energy utilization, climate change study, crop growth model, and etc. This research tried to explore the optimal combination of input meteorological factors and the machine learning methods for the estimation of daily solar radiation under different climatic conditions so as to improve the estimation accuracy. Based on the correlation between meteorological factors, different meteorological factor input combinations were established and the support vector machine method was used to estimate global solar radiation at 80 weather stations in four climatic regions of China mainland. The results showed that, the optimal combinations of input meteorological factors were different in the four different climatic zones in China mainland. Three meteorological factors of sunshine hours, extraterrestrial radiation, and air temperature had greater impacts on the solar radiation estimation. Adding the factor of precipitation could obviously improve the estimation accuracy in humid regions, but not remarkably in arid regions. Wind speed had very little influence on solar radiation estimation. The accuracies of machine learning methods were better than the Angstrom-Prescott formula and the multiple linear regression method. Among them, support vector machine and extreme learning machine were more appropriate. In some sites, the root mean square error of support vector machine method was even 20% less than that of the Angstrom-Prescott formula. In general, reasonable division of the areas and establishment of appropriate input combinations of meteorological factors according to the climatic conditions, combined with machine learning methods, can effectively improve the accuracy of solar radiation estimation.
机译：全球太阳辐射的值是潜在的蒸发估计，太阳能利用，气候变化研究，作物生长模型等的重要基本数据。该研究试图探讨输入气象因素和机器学习方法的最佳组合估算日常太阳辐射在不同气候条件下，以提高估计精度。基于气象因素之间的相关性，建立了不同的气象因子输入组合，并使用支持向量机方法在中国大陆四个气候区域估算全球太阳能辐射。结果表明，中国大陆四种不同气候区的输入气象因素的最佳组合不同。阳光小时，外星辐射和空气温度的三个气象因素对太阳辐射估计产生了更大的影响。增加降水因子可以显然提高潮湿地区的估计准确性，但在干旱地区没有显着。风速对太阳辐射估计影响很小。机器学习方法的准确性优于抗埃普雷斯特公式和多元线性回归方法。其中，支持向量机和极端学习机更合适。在某些地点，支持向量机方法的根均方误差甚至比埃克斯特罗姆 - 普雷屏式公式的20％小。一般来说，根据气候条件，合理分裂的地区和建立适当的流动因素组合，加上机器学习方法，可以有效提高太阳辐射估计的准确性。

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  • 来源
    《Energy Conversion & Management》 |2020年第9期|113111.1-113111.15|共15页
  • 作者

    He Chuan; Liu Jiandong; Xu Fang; Zhang Teng; Chen Shang; Sun Zhe; Zheng Wenhui; Wang Runhong; He Liang; Feng Hao; Yu Qiang; He Jianqiang;

  • 作者单位

    Northwest A&F Univ Inst Water & Soil Conservat State Key Lab Soil Eros & Dryland Farming Loess P Yangling 712100 Shaanxi Peoples R China|Chinese Acad Meteorol Sci State Key Lab Severe Weather Beijing 100081 Peoples R China|Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Yangling 712100 Shaanxi Peoples R China;

    Chinese Acad Meteorol Sci State Key Lab Severe Weather Beijing 100081 Peoples R China;

    Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Yangling 712100 Shaanxi Peoples R China|Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China;

    Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Yangling 712100 Shaanxi Peoples R China|Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China;

    Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Yangling 712100 Shaanxi Peoples R China|Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China;

    Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Yangling 712100 Shaanxi Peoples R China|Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China;

    Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Yangling 712100 Shaanxi Peoples R China|Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China;

    Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Yangling 712100 Shaanxi Peoples R China|Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China;

    Natl Meteorol Ctr Beijing 100081 Peoples R China;

    Northwest A&F Univ Inst Water & Soil Conservat State Key Lab Soil Eros & Dryland Farming Loess P Yangling 712100 Shaanxi Peoples R China|Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China;

    Northwest A&F Univ Inst Water & Soil Conservat State Key Lab Soil Eros & Dryland Farming Loess P Yangling 712100 Shaanxi Peoples R China;

    Northwest A&F Univ Inst Water & Soil Conservat State Key Lab Soil Eros & Dryland Farming Loess P Yangling 712100 Shaanxi Peoples R China|Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Yangling 712100 Shaanxi Peoples R China|Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China|Shaanxi Meteorol Bur Key Lab Ecoenvironm & Meteorol Qinling Mt & Loess Xian 710014 Shaanxi Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Solar radiation; Machine learning; Climatic zones; Input combination; Meteorological factor;

    机译：太阳辐射;机器学习;气候区;输入组合;气象因素;

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