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.
1 code implementation • 2 Jul 2022 • Yuhao Nie, Xiatong Li, Andea Scott, Yuchi Sun, Vignesh Venugopal, Adam Brandt
The dataset contains three years (2017-2019) of quality-controlled down-sampled sky images and PV power generation data that is ready-to-use for short-term solar forecasting using deep learning.