{"title":"Localized solar radiation zoning by combining spatially continuous estimates and Gaussian mixture models","authors":"Xuecheng Wang , Peiran Xie , Yiyi Xie , Hou Jiang","doi":"10.1016/j.jastp.2025.106432","DOIUrl":null,"url":null,"abstract":"<div><div>With the increasing role of solar energy in the global decarbonization, precise geographical zoning of solar radiation becomes crucial. Traditional methods of solar radiation zoning struggle to accurately distinguish subtle spatial and temporal differences in solar radiation due to both sparse ground-based observations and the requirement for a predefined zone number, which limits their applicability for the demands of distributed photovoltaic system. This study introduces a novel method for localized solar radiation zoning, integrating spatially continuous solar radiation data with a Gaussian mixture model. High-precision spatiotemporal estimates of solar radiation are achieved by employing deep learning algorithms to analyze meteorological satellite imagery and digital elevation model data. The use of an infinite Gaussian mixture model along with variational inference allows for the adaptive determination of the number of solar radiation zones. The case study in Guangxi Province shows that incorporating Digital Elevation Model data reduces the root mean square error of global solar radiation estimates from 134.06 W/m<sup>2</sup> to 87.68 W/m<sup>2</sup> and accurately reveals temporal and spatial variability in both global and diffuse solar radiation. This approach not only prevents overfitting when the predefined upper bound surpasses the actual number of zones but also facilitates the development of zoning schemes that can range from fine-grained, capturing subtle variations, to coarse-grained, focusing on overall patterns. The outcomes lay a solid foundation for localized regional assessment and efficient utilization of solar energy resources.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"268 ","pages":"Article 106432"},"PeriodicalIF":1.8000,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Atmospheric and Solar-Terrestrial Physics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364682625000161","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
With the increasing role of solar energy in the global decarbonization, precise geographical zoning of solar radiation becomes crucial. Traditional methods of solar radiation zoning struggle to accurately distinguish subtle spatial and temporal differences in solar radiation due to both sparse ground-based observations and the requirement for a predefined zone number, which limits their applicability for the demands of distributed photovoltaic system. This study introduces a novel method for localized solar radiation zoning, integrating spatially continuous solar radiation data with a Gaussian mixture model. High-precision spatiotemporal estimates of solar radiation are achieved by employing deep learning algorithms to analyze meteorological satellite imagery and digital elevation model data. The use of an infinite Gaussian mixture model along with variational inference allows for the adaptive determination of the number of solar radiation zones. The case study in Guangxi Province shows that incorporating Digital Elevation Model data reduces the root mean square error of global solar radiation estimates from 134.06 W/m2 to 87.68 W/m2 and accurately reveals temporal and spatial variability in both global and diffuse solar radiation. This approach not only prevents overfitting when the predefined upper bound surpasses the actual number of zones but also facilitates the development of zoning schemes that can range from fine-grained, capturing subtle variations, to coarse-grained, focusing on overall patterns. The outcomes lay a solid foundation for localized regional assessment and efficient utilization of solar energy resources.
期刊介绍:
The Journal of Atmospheric and Solar-Terrestrial Physics (JASTP) is an international journal concerned with the inter-disciplinary science of the Earth''s atmospheric and space environment, especially the highly varied and highly variable physical phenomena that occur in this natural laboratory and the processes that couple them.
The journal covers the physical processes operating in the troposphere, stratosphere, mesosphere, thermosphere, ionosphere, magnetosphere, the Sun, interplanetary medium, and heliosphere. Phenomena occurring in other "spheres", solar influences on climate, and supporting laboratory measurements are also considered. The journal deals especially with the coupling between the different regions.
Solar flares, coronal mass ejections, and other energetic events on the Sun create interesting and important perturbations in the near-Earth space environment. The physics of such "space weather" is central to the Journal of Atmospheric and Solar-Terrestrial Physics and the journal welcomes papers that lead in the direction of a predictive understanding of the coupled system. Regarding the upper atmosphere, the subjects of aeronomy, geomagnetism and geoelectricity, auroral phenomena, radio wave propagation, and plasma instabilities, are examples within the broad field of solar-terrestrial physics which emphasise the energy exchange between the solar wind, the magnetospheric and ionospheric plasmas, and the neutral gas. In the lower atmosphere, topics covered range from mesoscale to global scale dynamics, to atmospheric electricity, lightning and its effects, and to anthropogenic changes.