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Prediction of the day-ahead clear-sky downwelling surface solar irradiances using the REST2 model and WRF-CHIMERE simulations over the Arabian Peninsula

机译:使用REST2模型和阿拉伯半岛上的WRF-CHIMERE模拟来预测日前晴朗天空的下行井地面太阳辐照度

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摘要

The forecasting of the downwelling surface solar irradiances is required by the operators of solar powered plants to predict the power output of the plant. The purpose of this study is to use aerosol optical depth and columnar water vapor forecasts by the chemistry transport model CHIMERE, coupled with the Weather Research and Forecast (WRF) model, as inputs in the REST2 model to predict the day-ahead clear-sky global horizontal, direct normal and diffuse horizontal irradiances at hourly steps. A dataset of quality-assured and cloud-screened surface solar irradiance measurements collected from 44 sites scattered throughout Saudi Arabia was used to validate the predicted surface solar irradiances. The validation for the day-ahead forecasting of the clear-sky global horizontal irradiance for the data of all 44 stations combined exhibits a bias (relative to the mean reference value) of 0%, a relative root mean square error of 4% and a correlation coefficient of 0.993. Respectively, the clear-sky direct normal irradiance exhibits values of 0%, 15% and 0.769, while the clear-sky diffuse horizontal irradiance values are 0%, 35% and 0.542. The errors in the inputs are also investigated. The results are satisfactory and pave the way to the inclusion of the attenuation due to clouds with the final aim of predicting the surface solar irradiance under all-sky conditions.
机译:太阳能发电厂的运营商需要预测井下地面太阳辐照度,以预测发电厂的功率输出。这项研究的目的是使用化学迁移模型CHIMERE结合气溶胶光学深度和柱状水汽预报,再结合天气研究和预报(WRF)模型,将其作为REST2模型的输入来预测日前晴空以小时为单位的整体水平,直接法向和散布水平辐照度。从分布在沙特阿拉伯各地的44个地点收集的质量有保证并经过云屏蔽的表面太阳辐照度测量数据集用于验证预测的表面太阳辐照度。对所有44个站点的数据进行的晴天全球水平辐照度的日前预测的验证显示,偏差(相对于平均参考​​值)为0%,相对均方根误差为4%,并且相关系数为0.993。晴空直接法向辐照度分别为0%,15%和0.769,而晴空漫射水平辐照度值为0%,35%和0.542。输入中的错误也会被调查。结果令人满意,并为包括因云造成的衰减铺平了道路,最终目的是预测全天候条件下的表面太阳辐照度。

著录项

  • 来源
    《Solar Energy》 |2018年第3期|36-44|共9页
  • 作者单位

    Khalifa Univ Sci & Technol, Res Ctr Renewable Energy Mapping & Assessment, Masdar Inst, POB 54224, Abu Dhabi, U Arab Emirates;

    Khalifa Univ Sci & Technol, Res Ctr Renewable Energy Mapping & Assessment, Masdar Inst, POB 54224, Abu Dhabi, U Arab Emirates;

    Khalifa Univ Sci & Technol, Res Ctr Renewable Energy Mapping & Assessment, Masdar Inst, POB 54224, Abu Dhabi, U Arab Emirates;

    Khalifa Univ Sci & Technol, Res Ctr Renewable Energy Mapping & Assessment, Masdar Inst, POB 54224, Abu Dhabi, U Arab Emirates;

    KACARE, POB 2022, Riyadh 11451, Saudi Arabia;

    KACARE, POB 2022, Riyadh 11451, Saudi Arabia;

    KACARE, POB 2022, Riyadh 11451, Saudi Arabia;

    Khalifa Univ Sci & Technol, Res Ctr Renewable Energy Mapping & Assessment, Masdar Inst, POB 54224, Abu Dhabi, U Arab Emirates;

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

    Aerosols; Cloud-free; Chemistry transport; Solar forecasting;

    机译:气溶胶;无云;化学运输;太阳能预报;
  • 入库时间 2022-08-18 00:22:46

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