HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Theses

Deep learning for spatio-temporal forecasting - application to solar energy

Abstract : This thesis tackles the subject of spatio-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. We explore two main research directions for improving deep forecasting methods by injecting external physical knowledge. The first direction concerns the role of the training loss function. We show that differentiable shape and temporal criteria can be leveraged to improve the performances of existing models. We address both the deterministic context with the proposed DILATE loss function and the probabilistic context with the STRIPE model. Our second direction is to augment incomplete physical models with deep data-driven networks for accurate forecasting. For video prediction, we introduce the PhyDNet model that disentangles physical dynamics from residual information necessary for prediction, such as texture or details. We further propose a learning framework (APHYNITY) that ensures a principled and unique linear decomposition between physical and data-driven components under mild assumptions, leading to better forecasting performances and parameter identification.
Complete list of metadata

https://tel.archives-ouvertes.fr/tel-03590356
Contributor : Vincent Le Guen Connect in order to contact the contributor
Submitted on : Monday, May 2, 2022 - 4:36:33 PM
Last modification on : Saturday, May 7, 2022 - 3:19:01 AM

File

manuscript_these_vincent.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : tel-03590356, version 2

Collections

Citation

Vincent Le Guen. Deep learning for spatio-temporal forecasting - application to solar energy. Artificial Intelligence [cs.AI]. HESAM Université, 2021. English. ⟨tel-03590356v2⟩

Share

Metrics

Record views

62

Files downloads

1