A team of researchers has analyzed the impact of various cloud types on the predictions of solar radiation. They found prediction models that take into account the physics of how clouds interact with sunlight are more accurate than models that do not consider cloud types.
A research team has investigated how different cloud types influence solar forecast.
The scientists explained that clouds are a major challenge for predicting solar energy because of various and complex cloud radiation interactions, as a cloud Types show different macrophysical and microphysical properties, as well as various optical characteristics, which determine how the clouds scatter and absorb sunlight, which improves or decreasing the solar radiation. Cloud formation can also often change quickly, which can add uncertainty to solar predictions.
The data used by the Atmospheric Radiation Measurement (ARM) program of the US Department of Energy between 2001 and 2014 to analyze how eight cloud types influence the predictions of solar radiation. The cloud types treated in the study are cumulus, stratiform clouds, congestus, deep convective clouds, altostratus, altocumulus, cirros/anvil and cirrus.
The work that was built on earlier research by the team on data-driven models informed by Physics, which integrate cloud radiation physics to improve the accuracy of solar prediction. These models were tested on Real-World measurements of solar stream and cloud types of the SOUTH Great Plain (SGP) Central Facility Location.
The researchers found a clear hierarchy in the accuracy of the models based on the cloud type, where the model performs best against weak convective clouds, such as Cirrus, followed by stratiform clouds. It performed worse with strong convective clouds such as deep convective clouds.
“The trends we saw emphasized the complexity of predictions under certain cloud conditions,” said Shinjae Yoo, deputy assistant professor at Stony Brook University. “For example, in the case of deep convective clouds, which have more complex spatial structures with a dynamic and unpredictable nature, we have noticed a significant uncertainty in the results.”
The research also emphasized how to include information about cloud types in forecast models helps to improve predictions. The models that perform better in the physics of the way in which clouds interacted with sunlight performed better than previous models that did not consider cloud types, with the research team noticing an improvement accuracy of between 12% and 33%.
“These progress are important because they can help us to better predict the availability of solar energy under cloud conditions,” added Yangang Liu, senior scientist at Brookhaven National Laboratory. “As Zonne -Energy becomes a larger part of the energy letter, it will have more precise predictions to optimize how we use solar energy.”
The researchers said that further improvements in the predictions could be made by integrating cloud information directly into prediction models, by changing the physical formulation of cloud-radiation interaction, and by using more advanced machine learning models.
Their findings are available in “Use of physics to improve solar forecast: part ⅲ, effects of different cloud types“Available in the magazine Solar energy. The research was conducted by a team at Brookhaven National Laboratory in New York, in collaboration with researchers at Stony Brook University in New York, the Chinese Nanjing University of Information Science and Technology of the American National Department of Energy.
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