9 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
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…