Speed Prediction Research Articles

GAOformer: An adaptive spatiotemporal feature fusion transformer utilizing GAT and optimizable graph matrixes for offshore wind speed prediction

Wind speed prediction methods used to schedule wind power generation in advance is of great significance for ensuring grid safety and improving wind energy availability. However, most existing wind speed prediction models insufficiently extract spatial features for predicting wind speed at wind turbines stations, leading to less satisfying prediction results. To solve this issue, an adaptive spatiotemporal features fusion Transformer is proposed based on graph attention network (GAT) and optimizable graph matrix. First, a novel parameter optimization matrix is constructed using geographic information of cluster wind turbines, dynamic time warping (DTW), and maximum information coefficient (MIC) information to express the wind speed spatial correlation among these turbines. Second, graph attention network is used to extract spatial features from this matrix, sufficiently evaluating spatial similarity of wind speed series at different stations. Third, an embedding block is performed to characterize temporal features of cluster wind speed. Fourth, to effectively integrate these spatial and temporal features into spatial-temporal features, a new type of entangle block based on parameter optimization is proposed. Fifth, predicting values are obtained based on an improved Transformer by extracting effective features from spatiotemporal features with multi-head attention mechanism. Finally, Huber loss function is used to iteratively optimize the network parameters of the proposed model. To verify the effectiveness of the proposed model, five performance indexes, including MAE, MSE, MAPE, TIC and IA are employed. The results show that the proposed model outperforms other models including GAT-GRU, GAT-LSTM, GAT-Transformer, GCN-GRU, GCN-LSTM, GCN-Transformer, and Autoformer.

Research on vehicle speed prediction model based on traffic flow information fusion

Resource scarcity, global climate change and environmental pollution are increasingly constraining the development of the automotive industry. China proposes to reach the carbon peak by 2030; to reach the carbon neutral double carbon target by 2060 and gradually promote a green and low-carbon transition in energy development. The development of new energy vehicles is an important approach for China to realize its energy structure transformation in the automobile industry. HEV, as a transitional product of automobile energy transformation, has the advantages of both internal combustion engine vehicles and electric vehicles, which can improve the fuel efficiency and the emission problem of internal combustion engine vehicles and the range is longer compared to electric vehicles. One of the important aspects of HEV research is the design of whole vehicle energy management strategy based on the model predictions. Particularly, model-based predictive control is one of the mainstream energy management strategies nowadays, and its optimization effect is mainly subject to the model prediction accuracy. In this study, we constructed the ITS environment of a local roadway through simulation, compared the speed prediction effects of different speed prediction methods in different prediction time domains, and fused the historical information of vehicles (speed of the vehicle in front, distance, signal status, distance, and remaining time). It is found that N-BEATS is more effective in predicting vehicle speed in different prediction time domains, and the prediction accuracy of the speed prediction model is effectively improved after its fusion of multivariate information.

Short-term wind speed forecasts through hybrid model based on improved variational mode decomposition

ABSTRACT Scientific and accurate wind predictions are the basis for exploiting and utilizing wind energy. Combining the VMD and KELM, this research proposes a new hybrid model to capture the pattern of wind speed change, the parameters of which are optimized by the Spotted Hyena Optimizer(SHO) and Seagull Optimization Algorithm(SOA), respectively. The VMD is optimized by the SHO to achieve the purpose of adaptive decomposition of historical wind speed time series. After that, the parameters of the KELM are optimized by the SOA. Then, the decomposed wind speed series is input into the optimized KELM. The output prediction Intrinsic Mode Functions(IMFs) is added up to obtain the short-term wind speed prediction results. The measured wind speeds for four seasons were selected as the case study for the proposed model. The prediction results are compared with the measured wind speed series to verify the accuracy and reliability of the model. Besides, the prediction accuracy and stability of the proposed model are better than traditional prediction models, with stronger performance and lower computational cost. The results of case studies indicate that the proposed model can satisfy the need for actual applications.

Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction

Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, intelligent algorithm optimization, and error correction modules. First, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of the bidirectional gated recurrent unit (BiGRU) to ensure prediction quality. In order to eliminate the predictable components of the error further, a correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original SSA is enhanced by introducing a crazy operator and dynamic learning strategy, and the input weights and thresholds in the DELM are optimized by the ISSA to improve the generalization ability of the model. The actual data of wind farms are used to verify the advancement of the proposed model. Compared with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further used in the energy system.

An improved particle swarm algorithm-based method for kinetic modeling study of ammonia/air laminar flame speed

In recent years, ammonia has garnered increasing attention as a promising carbon-free fuel. Laminar flame speed is a critical property of ammonia fuel that has been extensively studied by researchers using chemical kinetics mechanisms. However, some deviations still remain in the numerical predictions. To further improve the prediction accuracy of the laminar flame speed for ammonia/air flame, an ammonia kinetic mechanism is developed in this work. Reactions from Gri-Mech 3.0, Li et al. mechanism, and Han et al. mechanism that have significant impact on laminar flame speed were first assembled to the Okafor et al. mechanism, and H/OH sub-mechanisms were merged to establish a kinetic mechanism containing 43 species and 142 reactions. A particle swarm algorithm with improved inertia weights is proposed and the fitness function for outputting laminar flame speed prediction error is customized based on Cantera codes, then forming the algorithm framework used to optimize the kinetic mechanism. The pre-exponential factor A, the temperature exponent n and the activation energy Ea of the four reactions in the merged mechanism that have high sensitivity to the laminar flame speed are selected as the independent variables for optimization, and the reactions rate constants corresponding to the high prediction accuracy of the laminar flame speed is finally obtained. The numerical prediction indicates a reduction in mean absolute percentage error of laminar flame speed from 22.563 % to 10.649 % using the optimization mechanism. The results of the sensitivity and reaction pathways analyses demonstrated that more H-related reactions were considered in the optimized mechanism, and the relative ROP of H-related reactions were adjusted at different equivalence ratios, resulting in higher or lower predictions of laminar flame speed compared with the unoptimized Okafor et al. mechanism. Ignition delay time as well as the species distribution of NH3, NO, and N2O was studied. The ignition delay time predictions using the optimized mechanism are in better agreement with the experiment data at P = 11 atm and 30 atm. The optimization mechanism demonstrated accurate predictions for NH3 and NO while exhibiting an overestimation for N2O, but the N2O prediction error is smaller compared to the Okafor et al. mechanism.

The Return Period Wind Speed Prediction of Beijing Urban Area Based on Short-Term Measured Wind Speed

To predict and explore the return period wind speed distribution in Beijing urban area, based on the short-term measured wind speed record, the measured independent storm peak samples are extracted and the Monte–Carlo numerical simulation method is used to randomly generate pseudo-independent storm peak samples with the same distribution as the independent storm peak samples. In addition, the appropriate threshold is used to select and extract the over-threshold independent storm peak samples from the measured samples. And, then, the return period wind speed of the whole wind direction is predicted. Further, the traditional Cook method is used to predict and analyze the return period wind speed of each wind direction according to the measured wind speed data. The results show that the return period wind speed prediction results of the whole wind direction corresponding to the simulated pseudo samples and the measured over-threshold samples are basically close to each other, and the maximum relative deviation is less than 7%. The mean relative deviation of the return period wind speed prediction results is about 10.6% between each wind direction and the whole wind direction, and the mean relative deviation is about 9.4% between the RPWS prediction result of each wind direction and the design value of the code. In addition, the return period wind speed prediction values of the west by southwest, northwest and north by northeast wind directions exceed the design value of the code, while the predicted values of the other wind directions are within the envelope range of the design value of the code. Finally, the research content of this paper can provide some reference for the wind resistance design analysis of building structures in Beijing area.

Research on speed predictive control system of intelligent connected vehicle based on V2X

To enhance the efficiency of urban road traffic and reduce waiting time at traffic lights, this paper proposes a V2X based speed prediction control method for intelligent connected vehicles. Firstly, this paper uses PreScan to build an intelligent network environment model based on V2X communication technology. Then, the traffic signal and target vehicle information transmitted by V2X are analyzed and processed, leading to the design of an intelligent connected vehicle speed prediction control system. Finally, comparative experiments were conducted using HIL between the conventional vehicle speed decision control system and the V2X-based intelligent connected vehicle speed prediction control system under various operating conditions. The experimental results demonstrate that the proposed speed prediction control system can reduce waiting time for traffic lights and enhance road traffic efficiency. Therefore, this paper’s designed speed prediction control system is both effective and feasible, providing a reference for future research on intelligent connected vehicle speed decision control.

A double‐layer neural network wind speed prediction framework based on training set segmentation and error correction

AbstractHighly accurate wind speed prediction is one of the effective ways to improve wind energy utilization. Therefore, this study proposes a novel double‐layer neural network (D‐NN) framework based on feedforward neural networks. First, a feedforward neural network with an extremely short training time is used as the basic learner to ensure that the D‐NN framework has a short training and optimization time. Then, the double‐layer neural network automatically segments and adjusts the length of the training set using an improved error correction method, thus improving the model's ability to capture sequence features and enhancing the model's generalization performance in a non‐directly‐connected superposition manner. Finally, a genetic algorithm is used to automatically optimize the hyperparameters of each sub‐neural network in the D‐NN and the optimal training set length of each sub‐neural network, resulting in the final optimized prediction model. The accuracy and reliability of the prediction models designed based on the D‐NN framework are verified by comparing several different models. The results of the five different prediction evaluation metrics show that the D‐NN framework can greatly improve the accuracy of wind speed prediction.

Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models

In recent years, the energy crisis has become increasingly severe, and global attention has shifted towards the development and utilization of wind energy. The establishment of wind farms is gradually expanding to encompass forested regions. This paper aims to create a Weather Research and Forecasting (WRF) model suitable for simulating wind fields in forested terrains, combined with a long short-term time (LSTM) neural network enhanced with attention mechanisms. The simulation focuses on capturing wind characteristics at various heights, short-term wind speed prediction, and wind energy assessment in forested areas. The low-altitude observational data are obtained from the flux tower within the study area, while high-altitude data are collected using mobile radar. The research findings indicate that the WRF simulations using the YSU boundary layer scheme and MM5 surface layer scheme are applicable to forested terrains. The LSTM model with attention mechanisms exhibits low prediction errors for short-term wind speeds at different heights. Furthermore, based on the WRF simulation results, a wind energy assessment is conducted for the study area, demonstrating abundant wind energy resources at the 150 m height in forested regions. This provides valuable support for the site selection in wind farm development.

Evaluating the performance of WRF in simulating winds and surface meteorology during a Southern California wildfire event

The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CA and intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event.

WGformer: A Weibull-Gaussian Informer based model for wind speed prediction

Accurate wind speed forecasting can improve energy management efficiency and promote the use of renewable energy. However, the inherent nonlinearity and fluctuation of wind speed make prediction challenging. To address these issues, we design an efficient Informer-based model, with improved calculation speed, forecasting accuracy and generalization ability. The proposed model in this paper reasonably integrates the Weibull-Gaussian transform, Informer and kernel mean square error loss and addresses the combination of various components. The Weibull-Gaussian transform is used as the data preprocessing module, which can remove non-Gaussian characteristics from the original data, and thus achieve noise reduction. The Informer is used as the main predictor, which can efficiently output accurate forecasting results based on an encoder-decoder architecture and self-attention mechanism. The kernel mean square error loss function, which shows strong robustness to outliers, is used to evaluate the nonlinearity of errors in reproducing kernel Hilbert space. To evaluate the performance of the proposed model, it is compared with several widely used models and state-of-the-art models. The experimental results indicate that the proposed model weakens the effect of outliers, yields high forecasting accuracy with mean square error = 0.35, and outperforms the baselines up to 8.5% on three datasets.

A short-term wind speed prediction method based on the BLS–RVM hybrid model

Abstract The study introduces a novel approach to short-term wind speed forecasting, which amalgamates statistical and machine learning techniques through the utilization of a hybrid model consisting of the broad learning system (BLS) and the relevance vector machine (RVM), to account for the nonstationary characteristics of wind speed data. Firstly, the initial wind speed time series is preprocessed using singular spectrum analysis to achieve noise reduction. Subsequently, the denoised wind speed time series is predicted using generalized learning system (BLS), and the prediction error is obtained. Finally, the prediction error of BLS is further predicted using RVM, and the final prediction result is obtained by combining it with the prediction results of BLS. By integrating multiple algorithms, this novel wind speed forecasting hybrid model improves prediction accuracy and can adapt to different wind speed characteristics and complex wind speed fluctuations. Through a case study, we find that this model outperforms other comparative models in terms of prediction, fully demonstrating its superiority.

Short-term wind speed forecasting based on a hybrid model that integrates PSO-LSSVM and XGBoost

Abstract A groundbreaking method is proposed to mitigate the impact of unpredictable fluctuations in wind velocity on wind power generation. This innovative approach integrates the particle swarm optimization (PSO)-least squares support vector machine (LSSVM) and XGBoost models in a harmonious manner. Initially, the raw wind speed data is subjected to wavelet threshold denoising to reduce noise and volatility. For short-term wind speed prediction, a PSO-LSSVM-XGBoost model is introduced. After the initial wind speed sequence undergoes wavelet threshold denoising, the enhanced sequence is forecasted using the LSSVM model, with its hyperparameters optimized through the PSO algorithm. The errors, obtained by subtracting the predicted values from the original data, are compensated using XGBoost. The final forecast results combine the rectified error data with the initial projected results. Experimental findings demonstrate the model’s remarkable capability to enhance prediction performance and accuracy.

A wind speed interval prediction method for reducing noise uncertainty

Due to the noise uncertainty, the conventional point prediction model is difficult to describe the actual characteristics of wind speed and lacks a description of the wind speed fluctuation range. In this paper, the kernel density estimation according to its error value is given, and then its fluctuation range is found to combine the prediction results of the test set to get its prediction range. Firstly, the singular spectrum analysis (SSA) is introduced to conduct the noise reduction, and variational modal decomposition (VMD) is performed to handle the sequences, then an improved slime mold algorithm (SMA) is proposed to optimize the VMD, and the stochastic configuration networks (SCNs) is applied to perform the prediction. Finally, the interval prediction results are calculated by fusing the point prediction error and kernel density estimation. The experimental results demonstrate that the proposed method can effectively reduce the noise interference in the wind speed prediction.

Wind speed prediction utilizing dynamic spectral regression broad learning system coupled with multimodal information

As the integration of wind energy into the power system increases, accurate wind speed prediction becomes crucial to ensure the reliable and economically efficient operation of the grid. The non-stationary, chaotic, and nonlinear characteristics of wind speed pose significant challenges for prediction models in uncovering the dynamic evolution process. To address these challenges, we proposed a wind speed prediction method based on the dynamic spectral regression broad learning system coupled with multimodal information (DSR-BLS). Firstly, we proposed a frequency density clustering-based mode decomposition (FDCMD) algorithm, which automatically transforms the non-stationary wind speed into multiple relatively stationary modal components. Next, we proposed the dynamic spectral regression (DSR) algorithm, which is based on dynamic-inner latent variable modeling and spectral regression. DSR can extract features and reconstruct the dynamic characteristics of wind speed through non-uniform embedding in the phase space. Finally, DSR-BLS is proposed to enhance the deterministic point prediction accuracy of the original BLS by utilizing multimodal features and dynamic features. The experiments show that DSR-BLS outperforms the comparative prediction methods in multi-step ahead prediction results.

Complex-valued artificial hummingbird algorithm for global optimization and short-term wind speed prediction

Environmental pollution and energy depletion have spurred the exploration of renewable energy sources. Wind energy, with its sustainability and eco-friendliness, stands out as a competitive option. However, its effectiveness relies on accurately predicting the fluctuating wind speeds, necessitating ongoing research in this area. In order to improve the accuracy of wind speed prediction, this article presents the first Complex-valued version of the Artificial Hummingbird Algorithm (CAHA), which utilizes the idea of doubles to increase the initial population diversity and enhance the algorithm's performance. Additionally, the article employs the CAHA to optimize the parameters of Artificial Neural Networks (ANNs). This marks the first application of the hybrid model to address the short-term wind speed prediction problem. The experiments begin with a performance evaluation of CAHA using the CEC2022 test set. Then, the CAHA is employed to optimize the parameters of six distinct ANNs to select the ANN model that can produce the best prediction result when synergized with CAHA. The results determine that the hybrid model based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) exhibits the best prediction performance. Furthermore, the performance of CAHA is compared with other classical optimization algorithms in short-term wind speed prediction problems based on ANFIS. Statistical results show that the ANFIS-CAHA model significantly improves the accuracy of short-term wind speed prediction, making it a potent tool for the integration of wind energy into smart grid engineering.

Reduced mechanisms for methanol oxidation under hydrothermal flames using a directed relation graph with sensitivity analysis and error propagation

AbstractA revised detailed mechanism (RDM) including 173 species and 1011 reactions for methanol oxidation under hydrothermal flames is developed via modifying a methanol gas‐phase combustion mechanism with pressure and thermodynamic corrections and validated with experimental data. Different skeletal mechanisms are generated via the directed relation graph (DRG), the DRG with error propagation (DRGEP), the DRG with sensitivity analysis (DRGSA), and the DRGEP with sensitivity analysis methods. Although the skeletal mechanisms have different reaction paths, the evolutions of the main species in the RDM are generally reproduced. The most compact mechanism of 12 species and 35 reactions is obtained via the DRGSA method with an error of 7.59%. The temperatures of hydrothermal flames predicted from the skeletal mechanisms with different reduction methods agree well with those from the RDM within most operating ranges except the 12‐species mechanism under hypoxic conditions. The increase in the reduction degree increases the error of the ignition delay time, while the reduced mechanisms have better adaptability in predicting the ignition delay time at higher preheating temperatures. The increases of preheating temperature and methanol concentration decrease the error in the prediction of the laminar flame speed with the reduced mechanisms. Moreover, the mechanisms with different reduction degrees have little effect on the extinction temperature, with an error of 4–8°C.

Enhancing rock and soil hazard monitoring in open-pit mining operations through ultra-short-term wind speed prediction

Wind speed exacerbates challenges associated with rock stability, introducing factors such as heightened erosion and the possibility of particle loosening. This increased sensitivity to erosion can result in material displacement, thereby compromising the overall stability of rock layers within the open-pit mining site. Therefore, accurate wind speed predictions are crucial for understanding the impact on rock stability, ensuring the safety and efficiency of open-pit mining operations. While most existing studies on wind speed prediction primarily focus on making overall predictions from the entire wind speed sequence, with limited consideration for the stationarity characteristics of the sequence, This paper introduces a novel approach for effective monitoring and early warning of geotechnical hazards. Our proposed method involves dividing wind speed data into stationary and non-stationary segments using the sliding window average method within the threshold method, validated by the Augmented Dickey-Fuller test. Subsequently, we use temporal convolutional networks (TCN) with dilated causal convolution and long short-term memory to predict the stationary segment of wind speed, effectively improving prediction accuracy for this segment. For the non-stationary segment, we implement complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to reduce sequence complexity, followed by TCN with an attention mechanism (ATTENTION) to forecast wind speed one step ahead. Finally, we overlay the predictions of these two segments to obtain the final prediction. Our proposed model, tested with data from an open-pit mining area in western China, achieved promising results with an average absolute error of 0.14 knots, mean squared error of 0.05 knots2, and root mean squared error of 0.20 knots. These findings signify a significant advancement in the accuracy of short-term wind speed prediction. This advancement not only enables the rapid assessment and proactive response to imminent risks but also contributes to geotechnical hazard monitoring in open-pit mining operations.

Short-term wind speed prediction based on improved Hilbert–Huang transform method coupled with NAR dynamic neural network model

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.

Dynamic rating method of traction network based on wind speed prediction

Dynamic rating method of traction network based on wind speed prediction