Lunche Wang , Yunbo Lu , Zhitong Wang , Huaping Li , Ming Zhang
{"title":"利用混合模型估算每小时太阳辐射并量化不确定性","authors":"Lunche Wang , Yunbo Lu , Zhitong Wang , Huaping Li , Ming Zhang","doi":"10.1016/j.rser.2024.114727","DOIUrl":null,"url":null,"abstract":"<div><p>Solar energy, considered to be the most abundant renewable resource, is one of the most effective methods for reducing carbon emissions. The quantification of the uncertainty in the model estimates due to the uncertainty in the input parameters has received very little attention, although models with different computational principles have been developed to estimate surface solar radiation. This study aims to establish and compare four hybrid models by coupling a physical model with machine learning models. Uncertainty in model estimations caused by uncertainty in cloud optical thickness, aerosol optical depth, precipitable water vapor, and total column ozone is quantified. The results of the radiative transfer model reveal a strong dependence on aerosol optical depth, cloud optical thickness, and total column ozone, but not on precipitable water vapor. The average uncertainties in the radiative transfer model estimates caused by the uncertainties in aerosol optical depth, cloud optical thickness, precipitable water vapor, total column ozone, and all of them together reached 37.76, 182.19, 22.76, 3.00, and 219.67 W m<sup>−2</sup> at all sites, respectively. Uncertainties in atmospheric parameters greatly limit the performance of hybrid models. RTM-RF has the strongest robustness compared to RTM-XGBoost, RTM-CatBoost, and RTM-LightGBM. The proposed hybrid model can be considered as a pertinent decision-support framework for the estimation of solar radiation components to further support clean energy utilization. Optimization of cloud inversion algorithms to improve the product accuracy of cloud optical properties over land and oceans is central to improving the accuracy of surface solar radiation estimates.</p></div>","PeriodicalId":418,"journal":{"name":"Renewable and Sustainable Energy Reviews","volume":null,"pages":null},"PeriodicalIF":16.3000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hourly solar radiation estimation and uncertainty quantification using hybrid models\",\"authors\":\"Lunche Wang , Yunbo Lu , Zhitong Wang , Huaping Li , Ming Zhang\",\"doi\":\"10.1016/j.rser.2024.114727\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Solar energy, considered to be the most abundant renewable resource, is one of the most effective methods for reducing carbon emissions. The quantification of the uncertainty in the model estimates due to the uncertainty in the input parameters has received very little attention, although models with different computational principles have been developed to estimate surface solar radiation. This study aims to establish and compare four hybrid models by coupling a physical model with machine learning models. Uncertainty in model estimations caused by uncertainty in cloud optical thickness, aerosol optical depth, precipitable water vapor, and total column ozone is quantified. The results of the radiative transfer model reveal a strong dependence on aerosol optical depth, cloud optical thickness, and total column ozone, but not on precipitable water vapor. The average uncertainties in the radiative transfer model estimates caused by the uncertainties in aerosol optical depth, cloud optical thickness, precipitable water vapor, total column ozone, and all of them together reached 37.76, 182.19, 22.76, 3.00, and 219.67 W m<sup>−2</sup> at all sites, respectively. Uncertainties in atmospheric parameters greatly limit the performance of hybrid models. RTM-RF has the strongest robustness compared to RTM-XGBoost, RTM-CatBoost, and RTM-LightGBM. The proposed hybrid model can be considered as a pertinent decision-support framework for the estimation of solar radiation components to further support clean energy utilization. Optimization of cloud inversion algorithms to improve the product accuracy of cloud optical properties over land and oceans is central to improving the accuracy of surface solar radiation estimates.</p></div>\",\"PeriodicalId\":418,\"journal\":{\"name\":\"Renewable and Sustainable Energy Reviews\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.3000,\"publicationDate\":\"2024-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Renewable and Sustainable Energy Reviews\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1364032124004532\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Renewable and Sustainable Energy Reviews","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364032124004532","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Hourly solar radiation estimation and uncertainty quantification using hybrid models
Solar energy, considered to be the most abundant renewable resource, is one of the most effective methods for reducing carbon emissions. The quantification of the uncertainty in the model estimates due to the uncertainty in the input parameters has received very little attention, although models with different computational principles have been developed to estimate surface solar radiation. This study aims to establish and compare four hybrid models by coupling a physical model with machine learning models. Uncertainty in model estimations caused by uncertainty in cloud optical thickness, aerosol optical depth, precipitable water vapor, and total column ozone is quantified. The results of the radiative transfer model reveal a strong dependence on aerosol optical depth, cloud optical thickness, and total column ozone, but not on precipitable water vapor. The average uncertainties in the radiative transfer model estimates caused by the uncertainties in aerosol optical depth, cloud optical thickness, precipitable water vapor, total column ozone, and all of them together reached 37.76, 182.19, 22.76, 3.00, and 219.67 W m−2 at all sites, respectively. Uncertainties in atmospheric parameters greatly limit the performance of hybrid models. RTM-RF has the strongest robustness compared to RTM-XGBoost, RTM-CatBoost, and RTM-LightGBM. The proposed hybrid model can be considered as a pertinent decision-support framework for the estimation of solar radiation components to further support clean energy utilization. Optimization of cloud inversion algorithms to improve the product accuracy of cloud optical properties over land and oceans is central to improving the accuracy of surface solar radiation estimates.
期刊介绍:
The mission of Renewable and Sustainable Energy Reviews is to disseminate the most compelling and pertinent critical insights in renewable and sustainable energy, fostering collaboration among the research community, private sector, and policy and decision makers. The journal aims to exchange challenges, solutions, innovative concepts, and technologies, contributing to sustainable development, the transition to a low-carbon future, and the attainment of emissions targets outlined by the United Nations Framework Convention on Climate Change.
Renewable and Sustainable Energy Reviews publishes a diverse range of content, including review papers, original research, case studies, and analyses of new technologies, all featuring a substantial review component such as critique, comparison, or analysis. Introducing a distinctive paper type, Expert Insights, the journal presents commissioned mini-reviews authored by field leaders, addressing topics of significant interest. Case studies undergo consideration only if they showcase the work's applicability to other regions or contribute valuable insights to the broader field of renewable and sustainable energy. Notably, a bibliographic or literature review lacking critical analysis is deemed unsuitable for publication.