Processing of Global Solar Irradiance and Ground-Based Infrared Sky Images for Solar Nowcasting and Intra-Hour Forecasting Applications

TL;DR

This paper presents a comprehensive data processing method combining infrared sky images and solar irradiance measurements to improve solar nowcasting and intra-hour forecasting by accounting for atmospheric and instrumental effects.

Contribution

It introduces a novel processing pipeline that removes biases, seasonal trends, and atmospheric scattering effects from sky images and irradiance data for enhanced solar forecasting accuracy.

Findings

Effective removal of cyclostationary biases in irradiance data

Successful classification of sky conditions into four categories

Improved modeling of atmospheric scattering effects

Abstract

The projection of shadows from moving clouds in the troposphere impacts energy generation in power grids using photovoltaic systems. This investigation proposes an efficient method of data processing for the statistical quantification of cloud features using infrared images and global solar irradiance measurements. The infrared images and the global solar irradiance measurements are acquired using a sky imager equipped with a commercial low-cost long-wave infrared radiometric camera and a pyranometer. The enclosure of the infrared camera is mounted on a solar tracker so that the Sun stays in the center of images throughout the day. We explain how to remove cyclostationary biases in global solar irradiance measurements. Seasonal trends are removed from the global solar irradiance time series, using the theoretical global solar irradiance to obtain the clear sky index time series. We introduce an atmospheric model to remove the effect of atmospheric scattering and the effect of the Sun's direct irradiance from infrared images. Scattering is produced by water spots and dust particles on the germanium lens of the camera enclosure. We explain how to remove the scattering effect produced by the germanium lens attached to the data acquisition system enclosure window of the infrared camera. An atmospheric condition model classifies the sky conditions in four different categories: clear sky, cumulus, stratus and nimbus. When an infrared image is classified in the category of clear sky, it is used to model the scattering effect produced by the germanium lens.