NOWCASTING PREDICTION OF SOLAR IRRADIANCE BASED ON FY-4A AND MACHINE LEARNING

被引：0
|
作者：
Jia D. [1 ]
Li K. [1 ]
Gao X. [2 ]
Gao Y. [3 ]
机构：
[1] College of Urban Environment, Lanzhou City University, Lanzhou
[2] Northwest Institute of Eco-Environment and Resources, CAS, Key Laboratory of Land Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences, Lanzhou
[3] Unit 94754 of the People’s Liberation Army, Jiaxing
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 04期
关键词：
clear sky model; forecasting; FY-4A; machine learning; solar irradiance;
D O I：
10.19912/j.0254-0096.tynxb.2022-1972
中图分类号：
学科分类号：
摘要：
In view of the characteristics of abundant radiation resources but lack of observation data in China，this study proposes a short-term solar irradiance forecasting method based on radiation observation data，remote sensing data，McClear，and random forest algorithm，and focuses on analyzing the impact of remote sensing data on radiation forecasting effectiveness. The results show that adding remote sensing data can optimize the forecasting effectiveness at different time horizons and significantly reduce the probability of large prediction errors with a mean absolute percentage error（MAPE）value exceeding 40%. Additionally，the improvement of the forecasting effectiveness with the addition of remote sensing data increases linearly with the time horizon. The difference range of normalized root mean square error（nRMSE）changes from 2.08% to 13.81%，the difference of normalized mean absolute error（nMAE）changes from 1.64% to 14.52%，the difference of R2 shows the most significant change with the time step，changing from -0.03 to -0.43. However，it is worth noting that adding satellite data will significantly increase the time required for model establishment and hyperparameter optimization. © 2024 Science Press. All rights reserved.
引用
下载
收藏
页码：578 / 583
页数：5
相关论文
共 24 条
  • [1] YANG D E, Et al., History and trends in solar irradiance and PV power forecasting：a preliminary assessment and review using text mining［J］, Solar energy, 168, pp. 60-101, (2018)
  • [2] JIA D Y, YANG L N, Et al., Evaluation of machine learning models for predicting daily global and diffuse solar radiation under different weather/pollution conditions ［J］, Renewable energy, 187, pp. 896-906, (2022)
  • [3] HU S L T, SHI J C，, LI M, Et al., A review of the estimation of downward surface shortwave radiation based on satellite data：methods，progress and problems［J］, Scientia sinica eerrae, 50, 7, pp. 887-902, (2020)
  • [4] JIANG J X, GAO X Q., Research progress on climate effect and influence mechanism of photovoltaic systems［J］, Plateau meteorology, 41, 4, pp. 953-962, (2022)
  • [5] GABRIEL R J, ANTONIO O S，, Et al., Hybrid techniques to predict solar radiation using support vector machine and search optimization algorithms：a review［J］, Applied sciences, 11, 3, (2021)
  • [6] ZHANG L， MA W，, ZHANG D., Stacked sparse autoencoder in PolSAR data classification using local spatial information［J］, IEEE geoscience and remote sensing letters, 13, 9, pp. 1359-1363, (2016)
  • [7] HINTON G E, SALAKHUTDINOV R R., Reducing the dimensionality of data with neural networks［J］, Science, 313, 5786, pp. 504-507, (2006)
  • [8] SRINIVASAN D., Automatic hourly solar forecasting using machine learning models［J］, Renewable and sustainable energy reviews, 105, pp. 487-498, (2019)
  • [9] MEGIA F A，, KURTZ B，, LEVIS A，, Et al., Cloud tomography applied to sky images：a virtual testbed［J］, Solar energy, 176, pp. 287-300, (2018)
  • [10] WILBERT S, Et al., Shadow camera system for the generation of solar irradiance maps［J］, Solar energy, 157, pp. 157-170, (2017)
123