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Various types of measurement techniques, such as Light Detection and Ranging (LiDAR) devices, anemometers, and wind vanes, are extensively utilized in wind energy to characterize the inflow. However, these methods typically gather data at limited points within local wind fields, capturing only a fraction of the wind field's characteristics at wind turbine sites, thus hindering detailed wind field analysis. This study introduces a framework using Physics-informed Neural Networks (PINNs) to assimilate diverse sensor data types. This includes line-of-sight (LoS) wind speed, velocity magnitude and direction, velocity components, and pressure. Moreover, the parameterized Navier–Stokes (N–S) equations are integrated as physical constraints, ensuring that the neural networks accurately represent atmospheric flow dynamics. The framework accounts for the turbulent nature of atmospheric boundary layer flow by including artificial eddy viscosity in the network outputs, enhancing the model's ability to learn and accurately depict large-scale flow structures. The reconstructed flow field and the effective wind speed are in good agreement with the actual data. Furthermore, a transfer learning strategy is employed for the online deployment of pre-trained PINN, which requires less time than that of the actual physical flow. This capability allows the framework to reconstruct wind flow fields in real time based on live data. In the demo cases, the maximum error between the effective wind speed reconstructed online and the actual value at the wind turbine site is only 3.7%. The proposed data assimilation framework provides a universal tool for reconstructing spatiotemporal wind flow fields using various measurement data. Additionally, it presents a viable approach for the online assimilation of real-time measurements. To facilitate the utilization of wind energy, our framework's source code is openly accessible. • A framework was proposed to assimilate wind flow fields from diverse data. • Line-of-sight speed, velocity vector/component, and pressure data are supported. • The parameterized N-S equations are integrated as physical constraints of PINN. • A transfer learning strategy is employed for the online deployment. [ABSTRACT FROM AUTHOR]

In this numerical study, we use a multi-scale version of physics-informed neural network (PINN) for wind field reconstruction from LiDAR measurements. The reference velocity field is obtained from high-fidelity large-eddy simulation of neutral atmospheric boundary layer, and the LiDAR measurement strategy is restricted to that of a real LiDAR. The multi-scale PINN reconstructs the velocity field by minimizing a combination of the L2-error with respect to the LiDAR measurements and residuals of the governing equations. Our most significant finding is that adding a multi-scale layer to PINN enables us to: (i) capture wider range of scales, (ii) reconstruct the flow field outside the LiDAR scanning area, and (iii) reach faster convergence. We also find that for volumetric flow reconstruction and to deal with uncertainty in the wind direction, the reconstruction accuracy can be greatly improved by employing multiple LiDAR devices. Overall, our results show that the normalized errors in the reconstructed wind field are lower than 5% for all the experiments conducted in this study and that the errors do not increase even in the presence of measurement noise levels of typical LiDAR. These findings show the feasibility of three-dimensional dynamic wind field reconstruction across large wind farms using LiDAR devices. • Successful wind field reconstruction using a multi-scale PINN from LiDAR data. • Multi-scale layer allows for capturing wider scales and faster convergence. • The neural network requires no prior training data for wind field reconstruction. • The study paves the way for real-time wind field reconstruction. [ABSTRACT FROM AUTHOR]

Accurate and reliable wind forecasts for urban blocks play a pivotal role in the construction of zero-energy communities by guiding the selection and placement of wind turbines and the aerodynamic design optimization of ducted openings. While relatively accurate wind fields are available based on numerical methods, their heavy computational cost and discontinuity make it necessary to explore an interactive and end-to-end method. In this study, we develop a physics-inspired and data-driven two-stage deep learning approach that can reconstruct complex wind fields precisely. The proposed method integrates a physical feature extraction model of the flow field with a sparse measurement data-driven error correction approach. In particular, a well-designed and well-trained flow field feature extraction model (original model) can preserve salient features of CFD modelling, while data-driven error correction techniques may harvest the uncertainty features and fill the remaining gaps between the original model predictions and the measured data. The proposed method is verified by a measured dataset from a community in Beijing. Experimental validation illustrates that the proposed algorithm successfully accomplishes wind field reconstruction in complex terrains using sparse datasets. We show that the proposed two-stage strategy exhibits significantly improved prediction results over the purely original method, with an average accuracy improvement of 47.17% and a maximum accuracy improvement of 72.59%. Overall, the proposed method delivers the potential in accurate wind field construction and urban wind energy forecasting. [Display omitted] • A physics-inspired and data-driven two-stage deep learning approach for wind field reconstruction is proposed. • A well-designed and well-trained flow field feature extraction model is developed. • An error correction method is proposed by considering the uncertainties present in real conditions. • Effectiveness of the proposed approach is verified via full-scale experiment. • The proposed algorithm exhibits good performance in simulating high wind speed field with significant fluctuations. [ABSTRACT FROM AUTHOR]

Physics-informed neural network (PINN)-based methods have recently been applied to reconstruct the spatiotemporal wind field based on LIDAR measurements. However, the accuracy of this reconstruction considerably degrades when LIDAR measurement noise exists. Unfortunately, LIDAR measurement noise is inevitable in the real world. To address this limitation, this paper proposes a novel anti-noise method to reconstruct the wind field based on real-world noisy LIDAR measurements. To this end, we first address the gradient vanishing problem in PINN's automatic differentiation process, which prevents the Navier-Stokes (NS) equations from being fully leveraged in wind field reconstruction. This problem is addressed by proposing a residual-connected PINN (RC-PINN) framework. Its advantage in solving the gradient vanishing problem is mathematically proven. Subsequently, by incorporating the NS equations into the RC-PINN, an anti-noise wind field reconstruction method is established. The key feature of this method is that, by employing the RC-PINN, the NS equations are more effectively utilized in the training process, thus improving its anti-noise capability. The numerical results show that, at a wide range of noise levels, the proposed method can achieve a significant improvement in reconstruction performance. Especially, at the noise level of commercially available LIDAR, the reconstruction results are quite satisfactory. Moreover, the proposed method can converge faster. • A residual-connected physics-informed neural network (RC-PINN) is proposed. • The advantage of the RC-PINN is proven mathematically. • An anti-noise wind field reconstruction method is established based on the RC-PINN. • The anti-noise mechanism behind the proposed method is demonstrated. [ABSTRACT FROM AUTHOR]

Short-term wind forecasting is important in updating wind electricity trading strategies, facility protection and more effective operation control. Physical based models, particularly those using computational fluid dynamics (CFD), are able to provide ever more detailed wind speed data. However, such methods involve handling a huge amount of CFD data, which is prohibitively time consuming for a short-term wind forecast in real situations. To solve this problem, Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) algorithms are applied in this study to reduce the dimensions of wind speed data and the proposed method is applied to reconstruct the wind field. Wind fields have successfully been reconstructed with good accuracy for the wind direction angles ranging from 0° to 90°. This method is validated by experimental data from a wind tunnel experiment. The accuracy of the proposed reconstruction algorithm increases with the sampling rate of the measurement and the locations of the sensors do not significantly affect the accuracy of the results. Gaussian noise introduced into the input signal does not significantly deteriorate the reconstruction quality. Results show that the proposed method can adequately be used to reconstruct the wind field for the models tested with a high degree of confidence. [ABSTRACT FROM AUTHOR]