1 code implementation • 20 Jun 2023 • Yuhao Nie, Eric Zelikman, Andea Scott, Quentin Paletta, Adam Brandt
Furthermore, we feed the generated future sky images from the video prediction models for 15-minute-ahead probabilistic solar forecasting for a 30-kW roof-top PV system, and compare it with an end-to-end deep learning baseline model SUNSET and a smart persistence model.
1 code implementation • 27 Nov 2022 • Yuhao Nie, Xiatong Li, Quentin Paletta, Max Aragon, Andea Scott, Adam Brandt
In this study, we present a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting (i. e., forecasting horizon less than 30 minutes), as well as related research areas which can potentially help improve solar forecasting methods, including cloud segmentation, cloud classification and cloud motion prediction.
1 code implementation • 3 Nov 2022 • Yuhao Nie, Quentin Paletta, Andea Scott, Luis Martin Pomares, Guillaume Arbod, Sgouris Sgouridis, Joan Lasenby, Adam Brandt
With more and more sky image datasets open sourced in recent years, the development of accurate and reliable deep learning-based solar forecasting methods has seen a huge growth in potential.
no code implementations • 7 Jun 2022 • Quentin Paletta, Guillaume Arbod, Joan Lasenby
In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework to improve intra-hour (up to 60-min ahead) irradiance forecasting.
no code implementations • 29 Nov 2021 • Quentin Paletta, Anthony Hu, Guillaume Arbod, Philippe Blanc, Joan Lasenby
Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification.
2 code implementations • 26 Apr 2021 • Quentin Paletta, Anthony Hu, Guillaume Arbod, Joan Lasenby
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency.
no code implementations • 1 Feb 2021 • 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 forecasting, addressing the intermittent energy production from solar panels.
no code implementations • 2 Dec 2020 • Quentin Paletta, Joan Lasenby
Improving irradiance forecasting is critical to further increase the share of solar in the energy mix.
no code implementations • 22 May 2020 • Quentin Paletta, Joan Lasenby
This work presents preliminary results on the application of deep Convolutional Neural Networks for 2 to 20 min irradiance forecasting using hemispherical sky images and exogenous variables.