(???????)Correct and remap solar radiation and photovoltaic power in China based on machine learning models
文献类型：期刊论文
| 作者 | Liu, Fa1; Wang, Xunming1,4; Sun, Fubao1,2,3,4; Wang, Hong1 |
| 刊名 | APPLIED ENERGY
 |
| 出版日期 | 2022-04-15 |
| 卷号 | 312页码:15 |
| 关键词 | Solar radiation Machine learning Empirical models Prediction Correction |
| ISSN号 | 0306-2619 |
| DOI | 10.1016/j.apenergy.2022.118775 |
| 通讯作者 | Wang, Xunming(xunming@igsnrr.ac.cn) ; Sun, Fubao(sunfb@igsnrr.ac.cn) |
| 英文摘要 | Accurate estimation of surface solar radiation (SSR) is crucial for photovoltaic (PV) systems design and solar PV power plants site selection. However, the SSR observations often suffer from inhomogeneity issues (e.g., aging equipment and instrument replacement) and low spatial-temporal coverage, which constrained the management and development of PV systems, particularly for countries with huge solar energy investments (e.g., China). To address them, we proposed seven different models (including four machine learning models, two empirical models and a multiple linear regression model) for accurate prediction of daily SSR using conventional meteorological data. Our results showed that the support vector machine (SVM) outperformed other models in estimating daily SSR. Based on SSR data corrected by the SVM model, the sharp downtrend in SSR (-9.64 W m(- 2) decade(-1)) due to the sensitivity drift of instrument aging before 1990 was moderated (-2.36 W m(-2) decade(-1)) in China; and the abnormal jump caused by instrument replacement in SSR observations during 1990-1993 was removed. Furthermore, by combining the advantages of large network of conventional meteorological data and SVM model, we extended the limited station-based SSR data (-100 meteorological stations) to include longer period and larger spatial coverage (2185 meteorological stations, 1971-2016) in China. By coupling the grid based method and PV power model, we created reliable SSR and PV power maps with higher temporal and spatial resolution over China. Spatially, the main distribution of high SSR and PV power were in the northwestern China, Tibetan Plateau and some coastal areas in the southern China. Temporally, the PV power experienced a significant drop during 1971-2016 (-1.94 kWh m(-2) decade(-1)) due to the significant decline in SSR (-0.91 W m(-2) decade(-1)). Above all, the new maps of SSR and PV power prepared here should support PV investments within the Chinese territory. |
| WOS关键词 | SUPPORT VECTOR MACHINE ; SURFACE ; OPTIMIZATION ; TEMPERATURE ; PREDICTION ; PRECIPITATION ; VARIABILITY ; POLLUTION ; IMPACTS |
| 资助项目 | National Natural Science Foundation of China[42025104] ; National Natural Science Foundation of China[42101011] ; National Natural Science Foundation of China[2021RC002] ; Key Frontier Program of Chinese Academy of Sciences[QYZDJSSW-DQC043] ; National Key Research and Development Program of China[2019YFA0606903] |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000774188700005 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Natural Science Foundation of China ; Key Frontier Program of Chinese Academy of Sciences ; National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/174125]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Xunming; Sun, Fubao |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China 3.Akesu Natl Stn Observat & Res Oasis Agroecosyst, Akesu 843017, Xinjiang, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 |
Liu, Fa,Wang, Xunming,Sun, Fubao,et al. (???????)Correct and remap solar radiation and photovoltaic power in China based on machine learning models [J]. APPLIED ENERGY,2022,312:15. |
| APA |
Liu, Fa,Wang, Xunming,Sun, Fubao,&Wang, Hong.(2022). (???????)Correct and remap solar radiation and photovoltaic power in China based on machine learning models .APPLIED ENERGY,312,15. |
| MLA |
Liu, Fa,et al." (???????)Correct and remap solar radiation and photovoltaic power in China based on machine learning models ".APPLIED ENERGY 312(2022):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。