Convolutional encoding and normalizing flows: a deep learning approach for offshore wind speed probabilistic forecasting in the Mediterranean Sea - Pôle AI & Ocean
Journal Articles Artificial Intelligence for the Earth Systems Year : 2024

Convolutional encoding and normalizing flows: a deep learning approach for offshore wind speed probabilistic forecasting in the Mediterranean Sea

Abstract

The safe and efficient execution of offshore operations requires short-term (1 to 6 hours ahead) high-quality probabilistic forecasts of metocean variables. The development areas for offshore wind projects, potentially in high depths, make it difficult to gather measurement data. This paper explores the use of deep learning for wind speed forecasting at an unobserved offshore location. The proposed convolutional architecture jointly exploits coastal measurements and numerical weather predictions to emulate multivariate probabilistic short-term forecasts. We explore both Gaussian and non-Gaussian neural representations using normalizing flows. We benchmark these approaches with respect to state-of-art data-driven schemes, including analog methods and quantile forecasting. The performance of the models, and resulting forecast quality, are analyzed in terms of probabilistic calibration, probabilistic and deterministic metrics, and as a function of weather situations. We report numerical experiments for a real case-study off the French Mediterranean coast. Our results highlight the role of regional numerical weather prediction and coastal in situ measurement in the performance of the post-processing. For single-valued forecasts, a 40% decrease in RMSE is observed compared to the direct use of numerical weather predictions. Significant skill improvements are also obtained for the probabilistic forecasts, in terms of various scores, as well as an acceptable probabilistic calibration. The proposed architecture can process a large amount of heterogeneous input data, and offers a versatile probabilistic framework for multivariate forecasting.
Fichier principal
Vignette du fichier
aies-AIES-D-23-0112.1.pdf (3.51 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04645705 , version 1 (12-07-2024)

Licence

Identifiers

Cite

Robin Marcille, Pierre Tandeo, Maxime Thiébaut, Pierre Pinson, Ronan Fablet. Convolutional encoding and normalizing flows: a deep learning approach for offshore wind speed probabilistic forecasting in the Mediterranean Sea. Artificial Intelligence for the Earth Systems, 2024, pp.1-41. ⟨10.1175/aies-d-23-0112.1⟩. ⟨hal-04645705⟩
50 View
27 Download

Altmetric

Share

More