Physics-guided machine learning for improved accuracy of the National Solar Radiation Database. (15th January 2022)

• Record Type: Journal Article
• Authors: Buster, Grant; Bannister, Mike; Habte, Aron; Hettinger, Dylan; Maclaurin, Galen; Rossol, Michael; Sengupta, Manajit; Xie, Yu
• Is Part Of: Solar energy. Volume 232(2022)
• Page Start: 483
• Page End: 492
• Publication Date: 2022-01-15
• Subjects: Solar resource data -- Machine learning -- Physics-guided neural networks -- Cloud properties -- Remote sensing -- Satellite-derived irradiance; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2022.01.004
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: A novel machine learning model is presented for remote sensing of cloud properties. The machine learning model is guided using a physics-based radiative transfer model. Resulting solar resource data is extensively validated against ground measurements. Significant improvements are shown in the accuracy of the solar resource data. Abstract: The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. On average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.