Speed Prediction Research Articles

An Improved Ensemble-Strategy-Assisted Wind Speed Prediction Method for Railway Strong Wind Warnings

Reliable short-term wind speed prediction is one of the core technologies in the strong wind warning system for railway applications, which is of great significance for ensuring the safety of high-speed train operations and ancillary railway facilities. To improve forecasting accuracy, decomposition-based methods have attracted extensive attention due to their superior ability to address complex data characteristics (e.g., nonstationarity and nonlinearity). Currently, there are two pre-processing schemes for decomposition-based methods, i.e., one-time decomposition and real-time decomposition. In order to apply them better, this paper first expounds the difference between them, based on a combination of DWT (discrete wavelet transform) and CKDE (conditional kernel density estimation). The results show that although the one-time decomposition-based method has an unexceptionable accuracy, it only can provide offline prediction and thus may not be practical. The real-time decomposition-based method possesses stronger practicability and is able to provide online prediction, but it has limited accuracy. Then, an improved ensemble strategy is developed by optimizing the selection of appropriate decomposed components to conduct the prediction on the basis of real-time decomposition. This improved ensemble strategy provides an effective guidance for this selective combination, including taking historical information into consideration in the data. Finally, numerical examples and practicality analysis using two groups of measured wind speed data demonstrate that the proposed method is effective in providing high-precision online wind speed prediction. For example, compared with CKDE, the average degrees of improvement achieved by the proposed method in terms of MAE, RMSE, and MRPE, are 16.25%, 17.66%, and 16.93, respectively, while those compared with the traditional real-time decomposition method are 17.11%, 18.54%, and 16.84, respectively.

Wind speed prediction using non-gaussian model based on Kumaraswamy distribution

ABSTRACT Wind power is a clean source of energy that not only helps in meeting the growing electricity demand but can also play a profound role in transforming the global energy distribution to mitigate the impact of climate change. Accurate forecasting of wind speed is a crucial factor of power system management. The present endeavor focuses on the development of a non-gaussian framework, Kumaraswamy seasonal autoregressive moving average (KSARMA), to model and forecast the wind speed data of 12 locations across the Indian subcontinent. The monthly wind speed dataset during the period 2003 to 2019 has been utilized to perform the analysis. Diagnostic analysis, Akaike’s information criterion (AIC), and conditional maximum likelihood estimator have been used to select the best-fit model for each location. The correlation coefficient between observed and predicted values for all the locations ranges from 0.77 to 0.93. The root mean squared error (RMSE) was found to lie in 0.04 to 0.16 whereas the mean absolute percentage error (MAPE) was observed in 0.06 to 0.2. A comparison of the proposed KSARMA model with the recently developed beta seasonal autoregressive moving average (βSARMA) and Kumaraswamy autoregressive moving average (KARMA) models has also been facilitated. Error estimates such as root mean squared error (RMSE) and mean absolute percentage error (MAPE) reveal that the KSARMA model outperforms the βSARMA and KARMA models except at the Kanyakumari location.

Comparison of coastal wind speed in Southeast Asian Countries using ANN backpropagation algorithm

This study aims to analyze the neural network backpropagation (ANN-BP) algorithm in seeing the development and comparison of wind speed. We estimated the wind speed of coastal areas with a case study of five countries in Southeast Asia. We used data for the last 10 years in the form of monthly data. The ANN-BP architecture uses input layers, three hidden layers, and one output layer with 12-25-10-1 architecture. From the simulation results in the training stage, we obtained information that the data model that is closest or follows the wind speed pattern with the smallest MSE value is the coastal area in Thailand with an MSE value of 0.00012 in the training stage and 0.00011 in the testing stage. Then, we also obtained that the average prediction of wind speed on the coast of Brunei Darusslam obtained the smallest value compared to other countries.

A novel wind power prediction model using graph attention networks and bi-directional deep learning long and short term memory

<span lang="EN-US">Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work.</span>

Maximum Power Point Tracking Based on Wind Speed Prediction

A control method for MPPT (maximum power point tracking) based on wind speed prediction is proposed to address the issue of slow attainment of optimal speed values by wind turbines in variable wind speed conditions in the wind power system. Initially, the wind speed variation value is accurately estimated using the SSA-BP neural network based on historical wind speed data. Subsequently, the MPP (maximum power point) is calculated considering the optimal blade-tip speed ratio, thereby enhancing the tracking speed of the system. Simulation results demonstrate that this method enables real-time estimation of wind speed variation, improves wind energy utilization rate, and maximizes the utilization of wind energy.

Nodal force error and its reduction for material point methods

This work shows that with the presence of a prestress, the discretized momentum equation using the material point method (MPM) or the dual domain material point (DDMP) method is inconsistent with the continuum version of the equation. This inconsistency leads to incorrect wave speed predictions in prestressed materials and numerical instabilities. The source of this inconsistency is the nodal force calculation using the material points.To address this issue, we introduce an auxiliary stress field, which can be regarded as a local background stress. We then reformulate the nodal force calculation, such that it only depends on the local stress variation. The effect of the prestress is then subtracted away in the nodal force calculation, and the consistencies of MPM and DDMP are then restored.This reformulation only requires simple modifications to the original nodal force in both MPM and DDMP. After implementing this reformulated nodal force in a numerical code and comparing the results with manufactured solutions, we found significant improvements not only in the accuracy of the solutions, but also in the numerical stability for both MPM and DDMP. With the reformulated nodal force, the DDMP results converge rapidly as the number of material points increases.

Wind speed prediction model based on DWT and Randon Forest

Wind energy is one of the fastest power generation technologies in the power generation industry and one of the most cost-effective methods of generating electrical power. For system reliability, improving highly appropriate wind speed forecasting methods is desirable. The wavelet transform is a powerful mathematical technique that converts an analyzed signal into a time-frequency representation. This technique helps forecast non-stationary time series. The objective is to evaluate the performance of the "DWT-Random Forest" model in predicting wind speed through a comparative analysis of performance metrics (MSE, RMSE, and MAPE) with similar studies. Our motivation is rooted in the pressing need to improve wind forecasting methods to optimize renewable energy generation. The method involved implementing the model, which presented performance metrics: MSE of 0.0099, RMSE of 0.0996, and MAPE of 0.0779. However, comparative analysis with previous studies reveals that our model demonstrates competitive performance. The main result of this study is the finding that the "DWT-Random Forest" model exhibits a respectable performance in predicting wind speed, although there is room for improvement.

A hybrid methodology using VMD and disentangled features for wind speed forecasting

Wind energy is gaining worldwide attention due to its renewable and ecological characteristics. The accurate prediction of wind speed presents a challenge due to its unpredictable and stochastic nature. In this study, a novel method for predicting wind speed is proposed. The proposed approach is called VMD-CoST-SVR and it involves the use of variational mode decomposition (VMD), contrastive learning of seasonal-trend representations (CoST), and support vector regression (SVR). The study utilizes the VMD technique to denoise the wind speed data. The CoST model, which employs contrastive learning, is then applied to the denoised wind speed data to extract the disentangled trend and seasonal features. The concatenated feature map of trend and seasonal features is subsequently fed to the SVR to predict the future wind speed. As the time interval increases, there is a corresponding decrease in the performance of predictive models. Addressing this, the proposed methodology has been subjected to training and validation for wind speed predictions at various time intervals, namely 5-min, 10-min, 15-min, 30-min, 1-hour, and 2-hour ahead using two experiments. This study utilizes wind speed data obtained from wind farms situated in Leicester and Portland. This validation has been accomplished through the use of seven individual models and seven hybrid models. The results of two experiments indicate that the proposed approach exhibited superior performance, resulting in a significant improvement across all time intervals evaluated.

Exploring Time Series Models for Wind Speed Forecasting: A Comparative Analysis

The sustainability and efficiency of the wind energy industry rely significantly on the accuracy and reliability of wind speed forecasting, a crucial concern for optimal planning and operation of wind power generation. In this study, we comprehensively evaluate the performance of eight wind speed prediction models, spanning statistical, traditional machine learning, and deep learning methods, to provide insights into the field of wind energy forecasting. These models include statistical models such as ARIMA (AutoRegressive Integrated Moving Average) and GM (Grey Model), traditional machine learning models like LR (Linear Regression), RF (random forest), and SVR (Support Vector Regression), as well as deep learning models comprising ANN (Artificial Neural Network), LSTM (Long Short-Term Memory), and CNN (Convolutional Neural Network). Utilizing five common model evaluation metrics, we derive valuable conclusions regarding their effectiveness. Our findings highlight the exceptional performance of deep learning models, particularly the Convolutional Neural Network (CNN) model, in wind speed prediction. The CNN model stands out for its remarkable accuracy and stability, achieving the lowest mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the higher coefficient of determination (R2). This underscores the CNN model’s outstanding capability to capture complex wind speed patterns, thereby enhancing the sustainability and reliability of the renewable energy industry. Furthermore, we emphasized the impact of model parameter tuning and external factors, highlighting their potential to further improve wind speed prediction accuracy. These findings hold significant implications for the future development of the wind energy industry.

Co-optimization of speed planning and cost-optimal energy management for fuel cell trucks under vehicle-following scenarios

Fuel cell hybrid electric heavy-duty vehicles play a key role to realize green and low-carbon travel. To cope with actual traffic environment, an advanced energy management strategy (EMS) is crucial to ensure vehicle’s efficiency and economy. In this paper, a predictive co-optimization control method is designed to achieve speed planning and energy management for the host vehicle with a fuel cell/Li-ion battery hybrid energy storage system under vehicle-following scenarios. Firstly, the host vehicle obtains the real-time speed of leading vehicle by communication technology, and speed prediction for the leading vehicle is implemented by fuzzy C-means clustering enhanced Markov Chain (FCM-MC) considering driving habits. Secondly, based on speed prediction results, the speed planning which aims to ensure safe inter-vehicle distance and minimize the demanded vehicular power is implemented to derive the speed curve of host vehicle. Finally, according to speed planning results, predictive energy management is applied to achieve power allocation between fuel cell (FC) and battery. The simulation results denote that the proposed speed prediction method can decrease forecast error effectively. The speed planning ensures the safe inter-vehicle distance. With speed prediction accuracy improved, the proposed hierarchical energy management strategy can reduce fuel cell degradation cost by 1.31%-3.48% and total operation cost by 0.75%-1.94%.

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting

Contemporary wind speed prediction research methodologies often employ two-stage decomposition preprocessing techniques to leverage the temporal correlation of wind speed. However, they frequently neglect to investigate the interpretability within the wind speed prediction model. To this end, a novel and interpretable hybrid forecasting model that combines two-layer decomposition, adaptive differential evolution with optional external archive (JADE), and temporal fusion transformers (TFT) is proposed. Primarily, on the basis of the linear-nonlinear decomposition criterion, a set of subcomponents is obtained using two-stage decomposition to fully extract the wind speed series prediction information for high-fluctuation and multi-resolution modes. Utilizing the JADE algorithm for intelligent and efficient optimization of parameter combinations in the TFT model guarantees the stability and reliability of the prediction model. Later, the obtained two-layer decomposition subseries are used as historical variables, and the meteorological and temporal data are entered into the TFT model as future known inputs. Empirical studies show that the proposed model demonstrates remarkable suitability and effectiveness in short-term wind speed forecasting. The utilization of the interpretable model has catalyzed significant advancements in wind speed prediction, while the analysis of its interpretable results empowers managers in formulating effective policies.

Spatial-temporal offshore wind speed characteristics prediction based on an improved purely 2D CNN approach in a large-scale perspective using reanalysis dataset

Accurate prediction of spatial–temporal wind speed with a large-scale perspective is vital for the wind farm dynamic regulation, the floating wind turbine deployment and its early warning. Machine-learning approaches have been applied for wind prediction to alleviate the computational challenges of traditional numerical models. Yet, most previous studies emphasized the forecasting accuracy employing location-specific dataset, such methodologies are site-specific and ignore the importance of spatial–temporal fidelity required by the 2D regional wind farm blueprint. Furtherly, the Recurrent Neural Networks based approach they employed exhibit low efficiency in terms of model convergence on the aspect of practical engineering purposes. This study firstly proposed an improved Unet-based machine learning method using residual learning to predict the spatial–temporal wind field in the North Atlantic Ocean with great potential wind-energy in a large spatial-scale perspective. Several state-of-the-art machine learning methods were established for a better performance comparison. A public reanalysis dataset with very high resolution (0.25° x 0.25°) was employed to verify the proposed method with multi-steps ahead predictions, the model performance was furtherly compared with the rest approaches under different criteria, consisting of Pearson correlation coefficient (PCC), Mean absolutely error (MAE), Root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE), all exhibiting considerable improvements. The overall forecasting RMSE for the proposed method dropped to 0.088 m/s and 0.087 m/s for wind component U and V correspondingly. Furtherly, comprehensive discussions were provided with both temporal and spatial perspective, revealing that the proposed model can offer an accurate 2D wind speed prediction along timeline (the PCC equals to 0.99). The proposed data-driven method presents promising potential to accurately preserve the spatial-resolved patterns which are variant in both spatial and temporal dimensions, and show superiority in terms of computational efficiency in terms of achieving large-scale wind pattern predictions.

A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm

Introduction: In the field of wind power generation, short-term wind speed prediction plays an increasingly important role as the foundation for effective utilization of wind energy. However, accurately predicting wind speed is highly challenging due to its complexity and randomness in practical applications. Currently, single algorithms exhibit poor accuracy in short-term wind speed prediction, leading to the widespread adoption of hybrid wind speed prediction models based on deep learning techniques. To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization.Methods: To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization. Firstly, the model incorporates an attention mechanism into the LSTM model to extract important temporal slices from each mode component, effectively improving the slice prediction accuracy. Secondly, two different search operators are introduced to enhance the original Salp Swarm Algorithm, addressing the issue of getting trapped in local optima and achieving globally optimal short-term wind speed predictions.Result: Through comparative experiments using multiple-site short-term wind speed datasets, this study demonstrates that the proposed VMD-AtLSTM-ASSA model outperforms other hybrid prediction models (VMD-RNN, VMD-BPNN, VMD-GRU, VMD-LSTM) with a maximum reduction of 80.33% in MAPE values. The experimental results validate the high accuracy and stability of the VMD-AtLSTM-ASSA model.Discussion: Short-term wind speed prediction is of paramount importance for the effective utilization of wind power generation, and our research provides strong support for enhancing the efficiency and reliability of wind power generation systems. Future research directions may include further improvements in model performance and extension into other meteorological and environmental application domains.

GBDT-based multivariate structural stress data analysis for predicting the sinking speed of an open caisson foundation

ABSTRACT Open caisson foundations are often used in large-span bridge construction because of their advantageous, and an open caisson foundation gradually sinks to a predetermined position via earth excavation. Excessive sinking speed may result in various construction risks (e.g. inclination and structural damage of the open caisson). To prevent these risks, it is important to analyse the sinking situation of the foundation and predict the sinking speed during the sinking process. A sinking speed prediction model is proposed based on the gradient boosting decision tree (GBDT) algorithm, and the model can extract the data features of the structural stress monitoring data to predict the sinking speed of open caissons. Taking the supersized open caisson foundation in the Changtai Yangtze River Bridge Project as a case study, the proposed model was validated by using the monitoring data of this foundation. The validation results of this project indicate that the proposed model has high prediction accuracy, short time consumption, a good prediction effect and high practicability. Based on the model’s prediction, an earth excavation scheme can be flexibly adjusted to prevent the potential construction risks of open caissons caused by excessive sinking speed.

A comprehensive wind speed prediction system based on intelligent optimized deep neural network and error analysis

At present, wind power is one of the most promising clean energy sources. Due to its high variability, accurate wind speed forecasting is an essential part of the wind power industry. This paper proposes a comprehensive forecasting system consisting of three parts: Firstly, the novel SC-EWT wind speed signal reconstruction system proposed in this article can effectively extract information from wind speed signals. Secondly, the QBGA-GRU model is used for signal reconstruction prediction, where QBGA is an improved genetic algorithm proposed in this article that can improve prediction accuracy. Finally, the IPSO-BLS algorithm is used to quickly analyze and predict prediction errors and further correct the predicted results. The system can provide users with adaptive signal decomposition, high-precision deep learning prediction and error analysis functions to achieve efficient and fast short-term wind speed forecasting, which can be used in the fields of wind power integration deployment and wind turbine early warning. The experiments show that the proposed prediction strategy has good predictive ability for wind speed samples in two regions, and its error is smaller than that of commonly used intelligent algorithms. Compared with other models, the proposed comprehensive prediction strategy has better predictive performance and smaller prediction errors than other combination models.

EEMD-ConvLSTM: a model for short-term prediction of two-dimensional wind speed in the South China Sea

EEMD-ConvLSTM: a model for short-term prediction of two-dimensional wind speed in the South China Sea

Traffic Speed Prediction Method Based on Spatiotemporal Sampling and LSTM

Accurate prediction of traffic speed is crucial for traffic management and planning. In order to solve the problems of low prediction efficiency of previous traffic speed prediction models and easy neglect of spatiotemporal characteristics, a traffic speed prediction method based on spatiotemporal sampling and LSTM model is proposed based on the 24-hour driving dataset of 4,000 taxis in Shanghai, and draw speed heat maps in different regions at different times to visualize the spatiotemporal characteristics of traffic speed. Experimental results show that the model has high prediction accuracy and good expansion potential.

Incremental forecaster using C–C algorithm to phase space reconstruction and broad learning network for short-term wind speed prediction

Wind power gains more and more attention from all over the world as a clean and renewable energy resource, and accurate prediction of wind speed has become a hot issue. This paper presents a novel incremental forecaster based on phase space reconstruction and broad learning network (BLN) for short-term prediction. First, time delay and embedding dimension which take an essential part in phase space reconstruction are determined by the C–C algorithm. Then, these optimal parameters are input to the BLN trained incrementally. Afterward, forecasting values are given by the output layer of BLN. Data collected from a wind farm is adopted for verifying the efficacy of this proposed model. Furthermore, five commonly used assessment indicators are applied to evaluate predictive performance of different models. Results show that the proposed model has the smallest prediction error, which performs better than the other models at one-step to three-step ahead forecasting, and this strength is attributed to address the problem of local optimum. Furthermore, the proposed model consumes less training time than the other models. Therefore, the proposed model tends to be promising for wind speed prediction of the big data era.

Optimal combined wake and active power control of large‐scale wind farm considering available power

AbstractThe high‐order nonlinear complex characteristics of traditional static wake model (SWM) restrict the fast optimization of wake effect. Therefore, this paper introduces a dynamic wake model (DWM) to describe the time‐varying wake characteristics of wind turbines (WTs) with low computational cost. The traditional SWM is linearized to derive the wake wind speed sensitivity coefficients, which represents the sensitivity of wake wind speed deficit with respect to the active power reference. Considering the natural propagation characteristics of wake effect, a wake delay function is added to realize the future wind speed prediction of different locations of the wind farm. And a joint control strategy of wake and active power based on model predictive control (MPC) is proposed to optimize the power contribution of each WT to minimize wake effects, and maximize total available power. Thus, the increased available power is able to satisfy the power demand from Transmission System Operator (TSO) for ancillary services and enhance power support capability. The control performance of the proposed control strategy is evaluated by simulations under constant and varying incoming wind speed.