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CESAR: A Convolutional Echo State AutoencodeR for High-Resolution Wind Forecasting

arXiv 2412.10578 ↗First posted 13 Dec 2024stat.AP stat.MLcited by 0

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Abstract (arXiv)

An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high resolution, calls for the development of nonlinear statistical models able to capture complex dependencies in space and time. This work introduces a Convolutional Echo State AutoencodeR (CESAR), a spatio-temporal, neural network-based model which first extracts the spatial features with a deep convolutional autoencoder, and then models their dynamics with an echo state network. We also propose a two-step approach to also allow for computationally affordable inference, while also performing uncertainty quantification. We focus on a high-resolution simulation in Riyadh (Saudi Arabia), an area where wind farm planning is currently ongoing, and show how CESAR is able to provide improved forecasting of wind speed and power for proposed building sites by up to 17% against the best alternative methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 68 references that are in the map. First posted on arXiv 13 Dec 2024.

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12 of 68 references.

References · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  3. 2015Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decompositioncited by 243
  4. 2021Activation Functions in Deep Learning: A Comprehensive Survey and Benchmarkcited by 155
  5. 2019Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networkscited by 129
  6. 2018Revisiting Small Batch Training for Deep Neural Networkscited by 102
  7. 2017Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Datacited by 17
  8. 2018Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecastingcited by 16
  9. 2017An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecastingcited by 12
  10. 2023Using Echo State Networks to Inform Physical Models for Fire Front Propagationcited by 4
  11. 2024Echo State Networks for Spatio-Temporal Area-Level Datacited by 2
  12. 2022A Bayesian Convolutional Neural Network-based Generalized Linear Modelcited by 1

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