Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning

• URL: http://arxiv.org/abs/2109.02411v1
• Date: Mon, 6 Sep 2021 14:46:20 GMT
• Title: Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning
• Authors: S. Ashwin Renganathan, Romit Maulik, Stefano Letizia, and Giacomo Valerio Iungo
• Abstract summary: We use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. We find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions, and the interaction between wakes. Physics-based models that capture the wake flow-field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional \emph{latent} space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow-fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Related papers

• SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification [17.175947741031674]
  Existing deep learning-based surrogate models facilitate efficient data generation, but fall short in uncertainty quantification, efficient parameter space exploration, and reverse prediction. We introduce SurroFlow, a novel normalizing flow-based surrogate model, to learn the invertible transformation between simulation parameters and simulation outputs. Our framework significantly reduces the computational costs while enhancing the reliability and exploration capabilities of scientific surrogate models.
  arXiv  Detail & Related papers  (2024-07-16T19:08:49Z)
• Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks [0.0]
  Wind represents a particularly challenging variable to model due to its high spatial and temporal variability. This paper presents a novel approach that integrates Gaussian processes (GPs) and neural networks to model surface wind gusts. We discuss the effect of different modeling choices, as well as different degrees of approximation, and present our results for a case study.
  arXiv  Detail & Related papers  (2024-05-21T09:07:47Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)
• VTAE: Variational Transformer Autoencoder with Manifolds Learning [144.0546653941249]
  Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables. The nonlinearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning. We show that geodesics and accurate computation can substantially improve the performance of deep generative models.
  arXiv  Detail & Related papers  (2023-04-03T13:13:19Z)
• Modeling Wind Turbine Performance and Wake Interactions with Machine Learning [0.0]
  Different machine learning (ML) models are trained on SCADA and meteorological data collected at an onshore wind farm. ML methods for data quality control and pre-processing are applied to the data set under investigation. A hybrid model is found to achieve high accuracy for modeling wind turbine power capture.
  arXiv  Detail & Related papers  (2022-12-02T23:07:05Z)
• End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning [7.850747042819504]
  This work proposes a surrogate model for the representation of wind turbine wakes based on a graph representation learning method termed a graph neural network. The proposed end-to-end deep learning model operates directly on unstructured meshes and has been validated against high-fidelity data. A case study based upon a real world wind farm further demonstrates the capability of the proposed approach to predict farm scale power generation.
  arXiv  Detail & Related papers  (2022-11-24T15:00:06Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Physics Informed Shallow Machine Learning for Wind Speed Prediction [66.05661813632568]
  We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in Italy. We train supervised learning algorithms using the past history of wind to predict its value at a future time. We find that the optimal design as well as its performance vary with the location.
  arXiv  Detail & Related papers  (2022-04-01T14:55:10Z)
• Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells [9.832272256738452]
  We propose a novel approach to data-driven modeling of a transient production of oil wells. We apply the transformer-based neural networks trained on the multivariate time series composed of various parameters of oil wells. We generalize the single-well model based on the transformer architecture for multiple wells to simulate complex transient oilfield-level patterns.
  arXiv  Detail & Related papers  (2021-10-12T15:00:45Z)
• Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer [56.34005280792013]
  We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations.
  arXiv  Detail & Related papers  (2021-01-19T16:14:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.