气候变化领域集成服务门户(CCPortal): Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging

CCPortal

DOI	10.3390/rs15092340
	Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging
	Wu, Lingxiao; Chen, Tianlu; Ciren, Nima; Wang, Dui; Meng, Huimei; Li, Ming; Zhao, Wei; Luo, Jingxuan; Hu, Xiaoru; Jia, Shengjie; Liao, Li; Pan, Yubing; Wang, Yinan
发表日期	2023
EISSN	2072-4292
卷号	15 期号:9
英文摘要	The Qinghai-Tibet Plateau is rich in renewable solar energy resources. Under the background of China's dual-carbon strategy, it is of great significance to develop a global horizontal irradiation (GHI) prediction model suitable for Tibet. In the radiation balance budget process of the Earth-atmosphere system, clouds, aerosols, air molecules, water vapor, ozone, CO2 and other components have a direct influence on the solar radiation flux received at the surface. For the descending solar shortwave radiation flux in Tibet, the attenuation effect of clouds is the key variable of the first order. Previous studies have shown that using Artificial intelligence (AI) models to build GHI prediction models is an advanced and effective research method. However, regional localization optimization of model parameters is required according to radiation characteristics in different regions. This study established a set of AI prediction models suitable for Tibet based on ground-based solar shortwave radiation flux observation and cloud cover observation data of whole sky imaging in the Yangbajing area, with the key parameters sensitively tested and optimized. The results show that using the cloud cover as a model input variable can significantly improve the prediction accuracy, and the RMSE of the prediction accuracy is reduced by more than 20% when the forecast horizon is 1 h compared with a model without the cloud cover input. This conclusion is applicable to a scenario with a forecast horizon of less than 4 h. In addition, when the forecast horizon is 1 h, the RMSE of the random forest and long short-term memory models with a 10-min step decreases by 46.1% and 55.8%, respectively, compared with a 1-h step. These conclusions provide a reference for studying GHI prediction models based on ground-based cloud images and machine learning.
关键词	Visible All-Sky image cloud cover global horizontal irradiation short-term forecast machine learning
英文关键词	SOLAR-RADIATION; NEURAL-NETWORKS; CLOUD DETECTION; PV; CLASSIFICATION; ACCURACY; ENERGY
WOS研究方向	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000987024400001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/282678
作者单位	Tibet University; Tibet University; Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS; Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS
推荐引用方式 GB/T 7714	Wu, Lingxiao,Chen, Tianlu,Ciren, Nima,et al. Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging[J],2023,15(9).
APA	Wu, Lingxiao.,Chen, Tianlu.,Ciren, Nima.,Wang, Dui.,Meng, Huimei.,...&Wang, Yinan.(2023).Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging.REMOTE SENSING,15(9).
MLA	Wu, Lingxiao,et al."Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging".REMOTE SENSING 15.9(2023).