Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 9; p. 2340
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 28.04.2023
• Subjects: Accuracy; Aerosols; Artificial intelligence; Atmospheric models; Carbon dioxide; Cloud cover; Clouds; Energy resources; Energy sources; Fluctuations; global horizontal irradiation; Horizon; Irradiation; Learning algorithms; Localization; Long short-term memory; Machine learning; Mathematical models; Measurement; Neural networks; Optimization; Parameter sensitivity; Prediction models; Radiation; Remote sensing; Short wave radiation; short-term forecast; Sky; Solar energy; Solar radiation; Sun; Time series; Visible All-Sky image; Water vapor; China; Tibet; Tibetan Plateau
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15092340

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.