A prediction model for vertical turbulence momentum flux above infinite wind farms

2021 ◽  
Vol 33 (5) ◽  
pp. 055108
Author(s):  
Mingwei Ge ◽  
Haoze Yang ◽  
Huan Zhang ◽  
Yingtao Zuo
Keyword(s):  
Prediction Model ◽  
Momentum Flux ◽  
Wind Farms ◽  
Vertical Turbulence

scholarly journals Implementing Large-Eddy Simulation Capability in a Compressible Mesoscale Model

2014 ◽  
Vol 142 (8) ◽  
pp. 2733-2750 ◽  
Author(s):  
Nicolas Gasset ◽  
Robert Benoit ◽  
Christian Masson
Keyword(s):  
Large Eddy Simulation ◽  
Mesoscale Model ◽  
Wind Farms ◽  
Test Cases ◽  
Eddy Simulation ◽  
Large Size ◽  
Large Eddy ◽  
Vertical Turbulence ◽  
Les Model ◽  
Future Work

Abstract The large size of modern wind turbines and wind farms triggers processes above the surface layer, which extend to the junction between microscales and mesoscales, and pushes the limits of existing approaches to predict the wind. The main objectives of this study are thus to introduce and evaluate an approach that will better account for physical processes within the atmospheric boundary layer (ABL), and allow for both microscale and mesoscale modeling. The proposed method, in which mathematical model and main numerical aspects are presented, combines a mesoscale approach with a large-eddy simulation (LES) model based on the Compressible Community Mesoscale Model (MC2). It is evaluated relying on a shear-driven ABL case allowing the authors to assess the model behavior at very high resolution as well as more specific numerical aspects such as the vertical discretization and time and space splitting of turbulence-related terms. The proposed LES-capable mesoscale model is shown to perform on par with other similar reference LES models, while being slightly more dissipative. A new vertical discretization of the turbulent processes eliminates a spurious numerical mode in the solution. Finally, the splitting of horizontal and vertical turbulence-related terms is shown to have no impact on the results of the test cases. It is thus demonstrated that the revised MC2 is suitable at both microscales and mesoscales, thus setting a strong foundation for future work.

scholarly journals A Markov Regime Switching Model for Ultra-Short-Term Wind Power Prediction Based on Toeplitz Inverse Covariance Clustering

2021 ◽  
Vol 9 ◽  
Author(s):  
Hang Fan ◽  
Xuemin Zhang ◽  
Shengwei Mei ◽  
Junzi Zhang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Wind Power ◽  
Regime Switching ◽  
Wind Farms ◽  
Power Prediction ◽  
Short Term ◽  
Markov Regime Switching ◽  
Switching Model ◽  
Switching Method

The rapid development of wind energy has brought a lot of uncertainty to the power system. The accurate ultra-short-term wind power prediction is the key issue to ensure the stable and economical operation of the power system. It is also the foundation of the intraday and real-time electricity market. However, most researches use one prediction model for all the scenarios which cannot take the time-variant and non-stationary property of wind power time series into consideration. In this paper, a Markov regime switching method is proposed to predict the ultra-short-term wind power of multiple wind farms. In the regime switching model, the time series is divided into several regimes that represent different hidden patterns and one specific prediction model can be designed for each regime. The Toeplitz inverse covariance clustering (TICC) is utilized to divide the wind power time series into several hidden regimes and each regime describes one special spatiotemporal relationship among wind farms. To represent the operation state of the wind farms, a graph autoencoder neural network is designed to transform the high-dimensional measurement variable into a low-dimensional space which is more appropriate for the TICC method. The spatiotemporal pattern evolution of wind power time series can be described in the regime switching process. Markov chain Monte Carlo (MCMC) is used to generate the time series of several possible regime numbers. The Kullback-Leibler (KL) divergence criterion is used to determine the optimal number. Then, the spatiotemporal graph convolutional network is adopted to predict the wind power for each regime. Finally, our Markov regime switching method based on TICC is compared with the classical one-state prediction model and other Markov regime switching models. Tests on wind farms located in Northeast China verified the effectiveness of the proposed method.

scholarly journals A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error

2021 ◽  
Vol 9 ◽  
Author(s):  
Haifeng Luo ◽  
Xun Dou ◽  
Rong Sun ◽  
Shengjun Wu
Keyword(s):  
Power Systems ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Method ◽  
Input Sequence ◽  
Wind Farms ◽  
Power Prediction ◽  
Cumulative Error ◽  
Wind Power Prediction ◽  
Accuracy And Stability

Wind power generation is likely to hinder the safe and stable operations of power systems for its irregularity, intermittency, and non-smoothness. Since wind power is continuously connected to power systems, the step length required for predicting wind power is increasingly extended, thereby causing an increasing cumulative error. Correcting the cumulative error to predict wind power in multi-step is an urgent problem that needs to be solved. In this study, a multi-step wind power prediction method was proposed by exploiting improved TCN to correct the cumulative error. First, multi-scale convolution (MSC) and self-attentiveness (SA) were adopted to optimize the problem that a single-scale convolution kernel of TCN is difficult to extract temporal and spatial features at different scales of the input sequence. The MSC-SA-TCN model was built to recognize and extract different features exhibited by the input sequence to improve the accuracy and stability of the single-step prediction of wind power. On that basis, the multi-channel time convolutional network with multiple input and multiple output codec technologies was adopted to build the nonlinear mapping between the output and input of the TCN multi-step prediction. The method improved the problem that a single TCN is difficult to tap the different nonlinear relationships between the multi-step prediction output and the fixed input. The MMED-TCN multi-step wind power prediction model was developed to separate linearity and nonlinearity between input and output to reduce the multi-step prediction error. An experimental comparative analysis was conducted based on the measured data from two wind farms in Shuangzitai, Liaoning, and Keqi, Inner Mongolia. As revealed from the results, the MAE and RMSE of the MMED-TCN-based multi-step prediction model achieved the cumulative mean values of 0.0737 and 0.1018. The MAE and RMSE metrics outperformed those of the VMD-AMS-TCN and MSC-SA-TCN models. It can be seen that the wind power prediction method proposed in this study could improve the feature extraction ability of TCN for input sequences and the ability of mining the mapping relationship between multiple inputs and multiple outputs. The method is superior in terms of the accuracy and stability of wind power prediction.

scholarly journals A Minimalistic Prediction Model to Determine Energy Production and Costs of Offshore Wind Farms

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 448
Author(s):  
Jens Nørkær Sørensen ◽  
Gunner Christian Larsen
Keyword(s):  
Prediction Model ◽  
Wind Turbines ◽  
Water Depth ◽  
Energy Production ◽  
Wind Farm ◽  
Cost Model ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
The Cost

A numerical framework for determining the available wind power and associated costs related to the development of large-scale offshore wind farms is presented. The idea is to develop a fast and robust minimal prediction model, which with a limited number of easy accessible input variables can determine the annual energy output and associated costs for a specified offshore wind farm. The utilized approach combines an energy production model for offshore-located wind farms with an associated cost model that only demands global input parameters, such as wind turbine rotor diameter, nameplate capacity, area of the wind farm, number of turbines, water depth, and mean wind speed Weibull parameters for the site. The cost model includes expressions for the most essential wind farm cost elements—such as costs of wind turbines, support structures, cables and electrical substations, as well as costs of operation and maintenance—as function of rotor size, interspatial distance between the wind turbines, and water depth. The numbers used in the cost model are based on previous but updatable experiences from offshore wind farms, and are therefore, in general, moderately conservative. The model is validated against data from existing wind farms, and shows generally a very good agreement with actual performance and cost results for a series of well-documented wind farms.

scholarly journals A Novel Multi-Step Cross-Decomposition Method Based on Wavelet Transform for Wind Power Prediction

2021 ◽  
Vol 252 ◽  
pp. 01015
Author(s):  
Jianxun Lang
Keyword(s):  
Wavelet Transform ◽  
Wind Speed ◽  
Prediction Model ◽  
Decomposition Method ◽  
Prediction Models ◽  
Wind Farms ◽  
Decomposition Methods ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Cross Decomposition

One of the main approaches to improve wind power prediction accuracy is to decompose wind-speed into different frequency-band components and use them as inputs of prediction model. Among the decomposition methods, wavelet transform is widely used due to its flexibility. However, the decomposition level and wavelet function need to be selected through trail-and-error, which is also called empirical decomposition method, because the effectiveness of a certain selection depends on the characteristic of wind farm and the prediction model. Therefore, it is difficult to find a general decomposition method that can be effective on different prediction models and wind farms. Aiming at this problem, a novel multi-step cross-decomposition method is proposed in this paper. The proposed method decomposes the wind-speed and power alternatively in each step, and after three steps of decomposition, the wind-speed can be decomposed to four different frequency-band components which will be used as the input of the prediction model. The prediction errors of proposed method and several empirical decomposition methods are compared on BPNN and SVM models. The results show that the proposed method is the only effective method on two prediction models for four wind farms.

1575: External Validation of Outcome Prediction Model for Ureteral/Renal Calculi

2005 ◽  
Vol 173 (4S) ◽  
pp. 427-427
Author(s):  
Sijo J. Parekattil ◽  
Udaya Kumar ◽  
Nicholas J. Hegarty ◽  
Clay Williams ◽  
Tara Allen ◽  
...  
Keyword(s):  
Prediction Model ◽  
Outcome Prediction ◽  
External Validation ◽  
Renal Calculi

Development of a headlamp dimming request prediction model

1975 ◽  
Author(s):  
Vivek D. Bhise ◽  
Thomas F. Swigart ◽  
Eugene I. Farber
Keyword(s):  
Prediction Model

Short-Version YLS-CMI: An Alternative Prediction Model for Juvenile Offenders at Intake

2009 ◽  
Author(s):  
Christina Campbell ◽  
Eyitayo Onifade ◽  
William Davidson ◽  
Jodie Petersen
Keyword(s):  
Juvenile Offenders ◽  
Prediction Model ◽  
Short Version
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