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Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events
Frontiers in Earth Science ( IF 2.0 ) Pub Date : 2021-03-04 , DOI: 10.3389/feart.2021.596860
Liexing Huang , Junfeng Kang , Mengxue Wan , Lei Fang , Chunyan Zhang , Zhaoliang Zeng

Solar radiation is the Earth’s primary source of energy and has an important role in the surface radiation balance, hydrological cycles, vegetation photosynthesis, weather and climate extremes. The accurate prediction of solar radiation is therefore very important in both the solar industry and climate research. We constructed 12 machine learning models to predict and compare daily and monthly values of solar radiation and a stacking model using the best of these algorithms were developed to predict solar radiation. The results show that meteorological factors (such as sunshine duration, land surface temperature and visibility) are crucial in the machine learning models. Trend analysis between extreme land surface temperatures and the amount of solar radiation showed the importance of solar radiation in compound extreme climate events. The gradient boosting regression tree (GBRT), extreme gradient lifting (XGBoost), Gaussian process regression (GPR) and random forest models performed better (poor) prediction capabilities of daily and monthly solar radiation. The stacking model, which included the GBRT, XGBoost, GPR and random forest models, performed better than the single models in the prediction of daily solar radiation but showed no advantage over the XGBoost model in the prediction of the monthly solar radiation. We conclude that the stacking model and the XGBoost model are the best models to predict solar radiation.

中文翻译:

使用不同机器学习算法的太阳辐射预测及其对极端气候事件的影响

太阳辐射是地球的主要能源,并且在地表辐射平衡,水文循环,植被光合作用,天气和极端气候方面具有重要作用。因此,对太阳辐射的准确预测在太阳能行业和气候研究中都非常重要。我们构建了12个机器学习模型来预测和比较日和月的太阳辐射值,并使用这些算法中的最佳算法开发了一个堆叠模型来预测太阳辐射。结果表明,气象因素(例如日照时长,地表温度和能见度)在机器学习模型中至关重要。极端地表温度和太阳辐射量之间的趋势分析表明,太阳辐射在复合极端气候事件中的重要性。梯度增强回归树(GBRT),极端梯度提升(XGBoost),高斯过程回归(GPR)和随机森林模型对每日和每月太阳辐射的预测能力都较好(较差)。包括GBRT,XGBoost,GPR和随机森林模型在内的堆叠模型在预测日太阳辐射方面表现优于单个模型,但在预测月太阳辐射方面却没有优于XGBoost模型的优势。我们得出的结论是,堆叠模型和XGBoost模型是预测太阳辐射的最佳模型。XGBoost,GPR和随机森林模型在每日太阳辐射的预测中比单一模型表现更好,但在每月太阳辐射的预测中却没有优于XGBoost模型的优势。我们得出结论,堆叠模型和XGBoost模型是预测太阳辐射的最佳模型。XGBoost,GPR和随机森林模型在每日太阳辐射的预测中比单一模型表现更好,但在每月太阳辐射的预测中却没有优于XGBoost模型的优势。我们得出的结论是,堆叠模型和XGBoost模型是预测太阳辐射的最佳模型。
更新日期:2021-04-30
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