Restrictive legislations on the use of fossil fuels encourage the research and development of
clean and renewable energies. Renewable energy is characterized by random behavior …

Merging machine learning with the power of quantum computing holds great potential for
data-driven decision making and the development of powerful models for complex datasets …

We use a full year (2018) of GOES-R satellite data to produce 5-minute resolved information
on cloud coverage for 7 Surface Radiation Budget Network (SURFRAD). The remote …

Semi-empirical satellite method is widely used in estimating global horizontal irradiance
(GHI), where various clear-sky models, cloud index (CI) and clear-sky index (CSI) derivation …

Cloud detection is an important task for remote sensing and solar resource modeling, and
an initial step toward more complex tasks like solar forecasting. Recent advances in remote …

Advancing probabilistic solar forecasting methods is essential to supporting the integration
of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art …

This work addresses challenges and opportunities in the evaluation of solar power plant
impacts, with a particular focus on thermal effects of solar plants on the environment and …

This work presents a new method to estimate atmospheric turbidity with improved accuracy
in estimating clear-sky irradiance. The turbidity is estimated by machine learning algorithms …

Solar forecasting has emerged as a cost-effective technology to mitigate the negative
impacts of intermittent solar power on the power grid. Despite the multitude of deep learning …

Short-term irradiance variability because of the passing clouds of unknown size, direction,
and speed is a key issue for power grid planners because of the unexpected fluctuation in …