Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements

Zhang, Jincheng; Zhao, Xiaowei

doi:10.1016/j.apenergy.2021.116641

Cited by 57 publications

(19 citation statements)

References 44 publications

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“…Another approach is to enhance the model with more advanced architecture (Wang et al, 2019). There are limited studies that focus on this approach by incorporating physics information (Zhang and Zhao, 2021). The advantage of using a physics-based approach is stated in three aspects.…”

Section: Discussionmentioning

confidence: 99%

Weather forecasting based on data-driven and physics-informed reservoir computing models

Mammedov

Olugu

Farah

2021

Environ Sci Pollut Res

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In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behaviour of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the out-performance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

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Section: Discussionmentioning

confidence: 99%

Weather forecasting based on data-driven and physics-informed reservoir computing models

Mammedov

Olugu

Farah

2021

Environ Sci Pollut Res

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“…Note that some recent works use a PGDDM approach to predict the flow field before reaching the wind farm (see, e.g., Refs. 89,90 ). These works have the same approach and methodology as the papers covered in this review but with a different objective; therefore, they are not covered here.…”

Section: Data-driven Wind-farm Flow Modelingmentioning

confidence: 99%

Data-driven fluid mechanics of wind farms: A review

Zehtabiyan-Rezaie,

Iosifidis,

Abkar

2022

Preprint

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With the growing number of wind farms over the last decades and the availability of large datasets, research in wind-farm flow modeling -one of the key components in optimizing the design and operation of wind farms -is shifting towards data-driven techniques. However, given that most current data-driven algorithms have been developed for canonical problems, the enormous complexity of fluid flows in real wind farms poses unique challenges for data-driven flow modeling. These include the high-dimensional multiscale nature of turbulence at high Reynolds numbers, geophysical and atmospheric effects, wake-flow development, and incorporating wind-turbine characteristics and wind-farm layouts, among others. In addition, data-driven wind-farm flow models should ideally be interpretable and have some degree of generalizability. The former is important to avoid a lack of trust in the models with end-users, while the most popular strategy for the latter is to incorporate known physics into the models. This article reviews a collection of recent studies on wind-farm flow modeling covering both purely data-driven and physics-guided approaches. We provide a thorough analysis of their modeling approach, objective, and methodology, and specifically focus on the data utilized in the reviewed works.

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“…The authors call for more concerted research efforts aiming to improve the capacity of deep neural networks for modeling earth systems data. Recently, the emergence of physics-informed deep learning has reinforced the integration of physical constraints as a research domain (Zhang and Zhao 2021;Rao, Sun, and Liu 2020;Wang et al 2020b;.…”

Section: Introductionmentioning

confidence: 99%

SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss

Klemmer

Acciaio

et al. 2022

AAAI

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From ecology to atmospheric sciences, many academic disciplines deal with data characterized by intricate spatio-temporal complexities, the modeling of which often requires specialized approaches. Generative models of these data are of particular interest, as they enable a range of impactful downstream applications like simulation or creating synthetic training data. Recently, COT-GAN, a new GAN algorithm inspired by the theory of causal optimal transport (COT), was proposed in an attempt to improve generation of sequential data. However, the task of learning complex patterns over time and space requires additional knowledge of the specific data structures. In this study, we propose a novel loss objective combined with COT-GAN based on an autoregressive embedding to reinforce the learning of spatio-temporal dynamics. We devise SPATE (spatio-temporal association), a new metric measuring spatio-temporal autocorrelation. We compute SPATE for real and synthetic data samples and use it to compute an embedding loss that considers space-time interactions, nudging the GAN to learn outputs that are faithful to the observed dynamics. We test our new SPATE-GAN on a diverse set of spatio-temporal patterns: turbulent flows, log-Gaussian Cox processes and global weather data. We show that our novel embedding loss improves performance without any changes to the architecture of the GAN backbone, highlighting our model's increased capacity for capturing autoregressive structures.

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Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements

Cited by 57 publications

References 44 publications

Weather forecasting based on data-driven and physics-informed reservoir computing models

Weather forecasting based on data-driven and physics-informed reservoir computing models

Data-driven fluid mechanics of wind farms: A review

SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss

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