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Tianle Yuan

Alumni

Publications

Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
Loubna Benabbou
Yoshua Bengio
Solar power harbors immense potential in mitigating climate change by substantially reducing CO…
openreview.net
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
Loubna Benabbou
Yoshua Bengio
The global integration of solar power into the electrical grid could have a crucial impact on climate change mitigation, yet poses a challen… (see more)ge due to solar irradiance variability. We present a deep learning architecture which uses spatio-temporal context from satellite data for highly accurate day-ahead time-series forecasting, in particular Global Horizontal Irradiance (GHI). We provide a multi-quantile variant which outputs a prediction interval for each time-step, serving as a measure of forecasting uncertainty. In addition, we suggest a testing scheme that separates easy and difficult scenarios, which appears useful to evaluate model performance in varying cloud conditions. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective use of solar power and the resulting reduction of CO
2023-07-28
ICML.cc/2023/Workshop/SynS_and_ML (published)
openreview.net
openreview.net
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
Loubna Benabbou
Yoshua Bengio
2023-07-28
ICML.cc/2023/Workshop/SynS_and_ML (published)
doi.org
openreview.net
Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks
Victor Schmidt
Mustafa Alghali
Kris Sankaran
Tianle Yuan
Yoshua Bengio
We introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large s… (see more)cale meteorological variables such as sea surface temperature and relative humidity. We show that our trained model can generate realistic CRFs from the corresponding meteorological observations, which represents a step towards a data-driven framework for stochastic cloud parameterization.
2020-02-10
ArXiv (preprint)
arxiv.org
arxiv.org