Improving prediction of surface solar irradiance variability by integrating observed cloud characteristics and machine learning. (1st September 2021)

• Record Type: Journal Article
• Authors: Riihimaki, Laura D.; Li, Xinya; Hou, Zhangshuan; Berg, Larry K.
• Is Part Of: Solar energy. Volume 225(2021)
• Page Start: 275
• Page End: 285
• Publication Date: 2021-09-01
• Subjects: Solar forecasting -- Variability -- Solar irradiance -- Machine learning -- ARM Southern Great Plains -- Clouds; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2021.07.047
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Characterized solar irradiance variability relationship with cloud properties. Observations taken from DOE Atmospheric Radiation Measurement site in Oklahoma. Seasonal analysis suggests relationship is site independent. Machine learning model developed to describe observations. Step towards Numerical Weather Model day ahead variability forecast. Abstract: A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R 2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.