气候变化领域集成服务门户(CCPortal): Study on the characteristics of solar shortwave irradiance and comparative analysis of short-term irradiance prediction of Yangbajing, Tibet

CCPortal

DOI	10.6038/cjg2022Q0381
	Study on the characteristics of solar shortwave irradiance and comparative analysis of short-term irradiance prediction of Yangbajing, Tibet
	Wu LingXiao; Wang YiNan; Wang Dui; Li Ming; Ciren Nima; Chen TianLu
发表日期	2023
ISSN	0001-5733
起始页码	3144
结束页码	3156
卷号	66 期号:8
英文摘要	In this research, using the measured solar short-wave irradiance data during the year of 2020 and 2021 at Yangbajing observation station in Tibet, the characteristics of radiation time series distribution is analyzed. And three solar irradiance prediction models tailor to Yangbajing area are established based on time series analysis, random forest (RF) and Prophet. Moreover, by comparing this three models, the applicability of three models and the method for improving the prediction accuracy of three models are explored. Our result shows that the monthly and diurnal variation of short-wave solar irradiance in this area display a bimodal inverted U and a unipolar inverted U distribution, respectively. Among the three models, RF is found to be the best model for predicting the solar irradiance in this area, with NRMSE (Normalized Root Mean Square Error) and R2 of 17.54% and 0.962, respectively. Both wavelet transform denoising and combination model can improve the prediction accuracy of the three models and the NRMSE by applying wavelet transform denoising is reduced by 4.82%similar to 12.94%.The NRMSE of autoregressive integrated moving average model (ARIMA) and Prophet of the error reciprocal weight combination model decreased by 35.22% and 25.12%, respectively. Furthermore, prediction time step differences also affect the prediction effect, and the prediction error of the model gradually becomes smaller with the time step. Therefore, machine learning models such as RF can be used to predict solar irradiance in Tibet, and the prediction accuracy can be improved through wavelet transforms, combined models, prediction time steps, etc., in order to meet the forecasting needs of local photovoltaic power generation for solar irradiance.
关键词	Solar irradiance Short-term forecasting ARIMA Random forest (RF)Prophet Yangbajing
英文关键词	NEURAL-NETWORK; WEATHER FORECASTS; RADIATION; MODEL; ARMA
WOS研究方向	Geochemistry & Geophysics
WOS记录号	WOS:001045501300002
来源期刊	CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283115
作者单位	Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS
推荐引用方式 GB/T 7714	Wu LingXiao,Wang YiNan,Wang Dui,et al. Study on the characteristics of solar shortwave irradiance and comparative analysis of short-term irradiance prediction of Yangbajing, Tibet[J],2023,66(8).
APA	Wu LingXiao,Wang YiNan,Wang Dui,Li Ming,Ciren Nima,&Chen TianLu.(2023).Study on the characteristics of solar shortwave irradiance and comparative analysis of short-term irradiance prediction of Yangbajing, Tibet.CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION,66(8).
MLA	Wu LingXiao,et al."Study on the characteristics of solar shortwave irradiance and comparative analysis of short-term irradiance prediction of Yangbajing, Tibet".CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION 66.8(2023).