What is it about?

While the earth is combatting the perils of climate change and fast-diminishing conventional energy resources, solar power has emerged as a sustainable energy solution for the future. However, the energy outputs of solar panels are heavily dependent on weather conditions, especially the intensity of cloud cover. Cloudy, overcast, or rainy days lead to decreased photovoltaic energy being converted from sunlight. In a recent study, authors present the development, validation, and performance of a model designed to estimate the optical properties of clouds. They used high-resolution imagery from a Geostationary Operational Environmental Satellite (GOES)-R sensor, along with Spectral Cloud Optical Property Estimation (SCOPE), a method that combines data from atmospheric parameters like ambient temperature and relative humidity to determine various optical properties of clouds at five minutes intervals throughout the day and night. The accuracy of the model was evaluated at seven climatically distinct regions in the US. SCOPE proved to be a solar forecasting method that was fast, accurate, widely applicable, compatible with different satellite systems, and had minimum data dependency.

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Why is it important?

New solar power generation systems need to take the effect of cloud cover into account during designing. However, a lack of accurate data leads to oversimplification of cloud optical properties during solar forecasting. SCOPE could plug this gap in knowledge by providing relevant and easy-to-access data. KEY TAKEAWAY: This new ‘remote-sensing’ method will help us predict cloud cover more accurately, which will help make solar power generation more predictable.

Read the Original

This page is a summary of: SCOPE: Spectral cloud optical property estimation using real-time GOES-R longwave imagery, Journal of Renewable and Sustainable Energy, March 2020, American Institute of Physics, DOI: 10.1063/1.5144350.
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