10 citations · 33 across the 10 of their papers we have counts for
10 papers
SolarBench: A global solar energy nowcasting benchmark
Yuhao Nie, Stephen Campbell, Quentin Paletta +18
As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art approaches increasingly apply…
SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT
Yuhao Nie, Eric Zelikman, Andea Scott +2
In recent years, deep learning-based solar forecasting using all-sky images has emerged as a promising approach for alleviating uncertainty in PV power generation. However, the sto…
Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?
Yuhao Nie, Quentin Paletta, Andea Scott +5
Solar forecasting from ground-based sky images has shown great promise in reducing the uncertainty in solar power generation. With more and more sky image datasets open sourced in…
Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey
Yuhao Nie, Xiatong Li, Quentin Paletta +3
Sky-image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty in solar power generation. However, one of the biggest…
Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions
Quentin Paletta, Guillaume Arbod, Joan Lasenby
Integration of intermittent renewable energy sources into electric grids in large proportions is challenging. A well-established approach aimed at addressing this difficulty involv…
SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting
Quentin Paletta, Anthony Hu, Guillaume Arbod +2
Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classifica…
ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy
Quentin Paletta, Anthony Hu, Guillaume Arbod +1
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of…
Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis
Quentin Paletta, Guillaume Arbod, Joan Lasenby
A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecastin…
A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications
Quentin Paletta, Joan Lasenby
Improving irradiance forecasting is critical to further increase the share of solar in the energy mix. On a short time scale, fish-eye cameras on the ground are used to capture clo…
Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting
Quentin Paletta, Joan Lasenby
Despite the advances in the field of solar energy, improvements of solar forecasting techniques, addressing the intermittent electricity production, remain essential for securing i…