Speed Prediction Research Articles

Research on energy management strategy for incremental range electric vehicles integrating speed prediction.

The main challenge facing current energy management strategies for extended-range electric vehicles is effectively balancing power demand and energy utilization to enhance fuel economy under complex and variable driving conditions. Therefore, to optimize the distribution between the two energy sources of extended-range electric vehicles and improve their fuel economy, this paper proposes an energy management strategy incorporating speed prediction. Firstly, the long short-term memory neural network speed prediction scheme is investigated, and its effectiveness under different cyclic conditions is verified. Secondly, the four hyperparameters of the long short-term memory neural network structure were optimized using the sparrow algorithm (SA) to further enhance the prediction accuracy of the long short-term memory speed prediction algorithm. After optimization, the mean square deviation and mean absolute error are reduced by 46.46% and 54.46%, respectively, compared with the pre-optimization period. Finally, an energy management strategy based on speed prediction was designed using the sparrow algorithm-long short-term memory model. The results show that the speed prediction-based energy management strategy reduces fuel consumption by 6.05% and 3.50% under the New European Driving Cycle and World Light Vehicle Test Cycle conditions, respectively, compared to the rule-based hybrid control strategy.

An innovative memory-enhanced Elman neural network-based selective ensemble system for short-term wind speed prediction

An innovative memory-enhanced Elman neural network-based selective ensemble system for short-term wind speed prediction

Prediction Model for Cutterhead Rotation Speed Based on Dimensional Analysis and Elastic Net Regression

The development and maturation of TBM (tunnel boring machine) technology have significantly improved the accuracy and richness of excavation data, driving advancements in intelligent tunneling research. However, challenges remain in managing data noise and parameter coupling, limiting the interpretability of traditional machine learning models regarding TBM parameter relationships. This study proposes a cutterhead rotation speed prediction model based on dimensional analysis. By utilizing boxplot methods and low-pass filtering techniques, excavation data were preprocessed to select appropriate operational and mechanical parameters. A dimensionless model was established and integrated with elastic net regression to quantify parameters. Using TBM cluster data from a water diversion tunnel project in Xinjiang, the accuracy and generalizability of the model were validated. Results indicate that the proposed model achieves high prediction accuracy, effectively capturing trends in cutterhead rotation speed while demonstrating strong generalizability.

Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network

Accurate and comprehensive wind speed forecasting is crucial for improving efficiency in offshore wind power operation systems in coastal regions. However, raw wind speed data often suffer from noise and missing values, which can undermine the prediction performance. This study proposes a systematic framework, termed VMD-RUN-Seq2Seq-Attention, for noise reduction, outlier detection, and wind speed prediction by integrating Variational Mode Decomposition (VMD), the Runge–Kutta optimization algorithm (RUN), and a Sequence-to-Sequence model with an Attention mechanism (Seq2Seq-Attention). Using wind speed data from the Shidao, Xiaomaidao, and Lianyungang stations as case studies, a fitness function based on the Pearson correlation coefficient was developed to optimize the VMD mode count and penalty factor. A comparative analysis of different Intrinsic Mode Function (IMF) selection ratios revealed that selecting a 50% IMF ratio effectively retains the intrinsic information of the raw data while minimizing noise. For outlier detection, statistical methods were employed, followed by a comparative evaluation of three models—LSTM, LSTM-KAN, and Seq2Seq-Attention—for multi-step wind speed forecasting over horizons ranging from 1 to 12 h. The results consistently showed that the Seq2Seq-Attention model achieved superior predictive accuracy across all forecast horizons, with the correlation coefficient of its prediction results greater than 0.9 in all cases. The proposed VMD-RUN-Seq2Seq-Attention framework outperformed other methods in the denoising, data cleansing, and reconstruction of the original wind speed dataset, with a maximum improvement of 21% in accuracy, producing highly accurate and reliable results. This approach offers a robust methodology for improving data quality and enhancing wind speed forecasting accuracy in coastal environments.

VSPNet: a vehicle speed prediction model incorporating transformer and BiLSTM

Abstract In recent years, with the increasing adoption of hybrid vehicles, energy management strategies 
have become a prominent research focus. Accurate Vehicle Speed Prediction (VSP) is a critical 
prerequisite for achieving optimal results in predictive energy management strategies. However, 
existing speed prediction algorithms fail to fully leverage vehicle data to enhance prediction 
accuracy. Therefore, a novel Vehicle Speed Prediction Net (VSPNet) is proposed in this study. 
Firstly, we constructed a combined cycle condition for model training through comprehensive 
analysis and analysed the vehicle feature parameters through the Random Forest (RF) algorithm 
and Pearson correlation analysis to select the best input feature parameters. Then a VSPNet 
speed prediction model is proposed based on the Transformer model. In the encoder part, firstly, 
by assigning weights to the input feature parameters and incorporating the temporal attention 
mechanism, the model is made to make better use of the input features from two dimensions, 
and at the same time the Transformer model's encoder based on positional coding combined 
with Bi-directional Long Short-Term Memory (BiLSTM) belonging to Recurrent Neural 
Networks(RNN), which is used as a decoder to better catch and handle long-term dependencies 
in sequence data. Finally, a comparative experiment between VSPNet and the classical speed 
prediction models was carried out. The proposed VSPNet model reduces the RMSE by 37%, 
22%, and 20% and MAE by 39%, 25, and 24% compared to the LSTM model for the prediction 
time horizons of 3s, 5s, and 8s. The RMSE is reduced by 47%, 28%, and 7%, and the MAE is 
reduced by 47%, 30, and 9% compared to the Transformer model for the prediction time 
horizons of 3s, 5s, and 8s. The experimental results demonstrate the superiority of this speed 
prediction model.

MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting

Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as difficulty in capturing complex spatio-temporal dependencies. To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. Then, a multivariate spatio-temporal correlation module is used to capture the global spatio-temporal dependencies between variables and model their interactions. Experimental results on the ERA5-LAND dataset show that, compared to the ConvLSTM, UNET, and SimVP models, the MIESTC model reduces RMSE by 14.60%, 8.64%, and 10.41%, respectively, for a 1 h prediction duration. For a 6 h prediction duration, the corresponding reductions are 13.91%, 8.20%, and 6.95%, validating its superior performance in short-term wind speed forecasting. Furthermore, an analysis of variable impacts reveals that U10, V10, and T2M play dominant roles in wind speed prediction, while TP exhibits a relatively lower impact, aligning with the results of the correlation analysis. These findings underscore the potential of MIESTC as an effective and reliable tool for short-term wind speed prediction.

Uncertainty prediction of wind speed based on improved multi-strategy hybrid models

<p>Accurate interval prediction of wind speed plays a vital role in ensuring the efficiency and stability of wind power generation. Due to insufficient traditional wind speed interval prediction methods for mining nonlinear features, in this paper, a novel interval prediction method was proposed by combining improved wavelet threshold and deep learning (BiTCN-BiGRU) with the nutcracker optimization algorithm (NOA). First, NOA was used to optimize the wavelet transform (WT) and BiTCN-BiGRU. Second, we applied NOA-WT to smooth the wind speed data. Then, to capture nonlinear features of time series, phase space reconstruction (PSR) was utilized to identify chaotic characteristics of the processed data. Finally, the NOA-BiTCN-BiGRU model was built to perform wind speed interval prediction. Under the same hyperparameters and network structure settings, a comparison with other deep learning methods showed that the prediction interval coverage probability (PICP) and prediction interval mean width (PIMW) of NOA-WT-BiTCN-BiGRU model achieves the best balance, with good prediction accuracy and generalization performance. This research can provide reference and guidance for nonlinear time-series interval prediction in the real world.</p>

A novel hybrid deep learning model for ultra-short-term prediction of wind speed

A novel hybrid deep learning model for ultra-short-term prediction of wind speed

Detonation product equation of state for overdriven detonations in triaminotrinitrobenzene-based plastic-bonded explosive

Overdriven detonation waves with non-stationary, high-pressure states can be produced by high-velocity impacts or by converging detonation waves. A precise equation of states (EOS) of the detonation products is essential to evaluate detonation performance and working capacity. The Jones-Wilkins-Lee (JWL) EOS and its modified forms have an uneven ability in describing the states of detonation products; furthermore, the accuracy of the calculated sound speed is inadequate. This problem is solved by an improved EOS, which is presented by introducing a variable Grüneisen coefficient within JWL. First, the Hugoniot parameters are calculated based on JWL, Jones-Wilkins-Lee-Lee, and Jones-Wilkins-Lee-Tang, and the maximum errors in the prediction of the sound speed are all significant, ranging from 5% to 15%. An excellent agreement is obtained using our modified JWL EOS for Hugoniot pressure over a wide range from initial pressure to 90 GPa, and in the meantime the sound speed is calculated more accurately, with the maximum error being reduced to 2.14%. Then, an experiment of the head-on collisions of detonation waves is carried out to assess the viability of our optimized EOS for characterizing the dynamic evolution of the overdriven detonation process. Furthermore, the hydrodynamic code is modified to incorporate the improved EOS and the numerical simulation is implemented. A comparison between the numerical results and the experimental data confirms the applicability of the improved EOS, and it indicates that a more accurate EOS is obtained for the description of the overdriven detonation phenomenon.

A novel deep reinforcement learning-based predictive energy management for fuel cell buses integrating speed and passenger prediction

A novel deep reinforcement learning-based predictive energy management for fuel cell buses integrating speed and passenger prediction

Research on ship speed prediction based on time series imaging and deep convolutional network fusion method

Research on ship speed prediction based on time series imaging and deep convolutional network fusion method

A novel spatiotemporal fusion wind speed prediction method under wind farm control center: BiTCN–transformer–cross-attention

A novel spatiotemporal fusion wind speed prediction method under wind farm control center: BiTCN–transformer–cross-attention

A multi-scale component feature learning framework based on CNN-BiGRU and online sequential regularized extreme learning machine for wind speed prediction

A multi-scale component feature learning framework based on CNN-BiGRU and online sequential regularized extreme learning machine for wind speed prediction

Challenges and potential solutions for the global optimization of "Offshore Wind Energy — Multi-Marine Resources" integration system in the Beibu Gulf of Guangxi

<p>The development of a national energy base and modern energy system in the Beibu Gulf of Guangxi requires an innovative energy system. General energy system only consists of a single marine energy resource, this work introduces an "Offshore Wind Energy—Multi-Marine Resources" integration system, which distinctively centers on offshore wind power while incorporating seawater hydrogen production, pumped storage, seawater desalination, marine aquaculture, and other marine resource utilization complexes. Its potential challenges during its future construction and potential solutions for the global optimization that need to be addressed are as follows: 1) creating a high-precision wind speed prediction model across multiple scales; 2) developing a global optimization model for the system under multiple uncertainties; and 3) proposing a resilience assessment method for systems subjected to unconventional external shocks. This integration system can contribute to the comprehensive development of marine resources and the establishment of a national comprehensive energy base in Guangxi Province and around the world.</p>

Machine learning approaches in predicting the wind power output and turbine rotational speed in a wind farm

ABSTRACT Accurate wind energy forecasting has become increasingly important to effectively manage the energy produced by wind turbine power plants and optimize their operational performance. In this study, several artificial intelligence techniques are recommended to simulate turbine rotation speed to predict wind energy production 10 minutes ahead. Four tools are employed: The fuzzy c-means (FCM) approach of adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), grid partitioning (GP) method of adaptive neuro-fuzzy inference system and subtractive clustering (SC) algorithm of adaptive neuro-fuzzy inference system were used for predictions. These methods use historical data as input for the physical parameters to be estimated and estimate the subsequent value as the output. Thus, using only historical data, future values of the considered parameter can be easily predicted without the need for other physical parameters, such as meteorological data or data regarding the design of the mechanical installation, or without solving complex differential equations containing many unknowns. In the study, wind power and rotor rotational speed data from one wind turbine operating in a wind farm is taken into consideration. Among 34 models, LSTM is shown to perform best in capturing real observed wind turbine parameters. In the estimations of wind output power of wind turbine, it is reported that the rates of evaluation criteria were computed as 136.04 kW MAE, 242.64 kW RMSE, and 0.9711 R. Besides, in the predictions of turbine rotational speed, it is notified that 0.72 rpm MAE, 1.16 rpm RMSE, and 0.9452 R values were computed. On the other hand, among the generated ANFIS models, ANFIS-FCM model yields best accurate results with 138.69 kW MAE, 244.40 kW RMSE and 0.9708 R values in wind power, 0.73 rpm MAE, 1.17 rpm RMSE and 0.9451 R values in turbine rotor rotation.

Wind speed prediction by utilizing geographic information system and machine learning approach: A case study of Karabük province in Türkiye

Wind speed prediction by utilizing geographic information system and machine learning approach: A case study of Karabük province in Türkiye

Deep learning framework for wind speed prediction in Saudi Arabia

Deep learning framework for wind speed prediction in Saudi Arabia

Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning system

AbstractThe inherent randomness and intermittent nature of wind speed fluctuations pose significant challenges in accurately predicting future wind speeds. To address this complexity, a wind speed prediction model based on a multiscale temporal‐preserving embedding broad learning system (MTPE‐BLS) is proposed. MTPE‐BLS used the localised behaviour of wind speed data, which is simpler to model and analyse than global patterns. Firstly, frequency clustering‐based variational mode decomposition (FC‐VMD) is proposed to deal with the non‐stationary wind speed data into multiple intrinsic mode functions (IMFs). Then, temporal‐preserving embedding (TPE) is proposed to extract the underlying temporal manifold structure from the decomposed IMFs. Finally, the extracted features are mapped into the broad learning system (BLS) to establish an accurate prediction model. Experimental results on two real‐world wind speed datasets demonstrate the best performance of the proposed MTPE‐BLS model compared to that of others. Compared to the original BLS, the MTPE‐BLS achieves significant improvements, reducing the root mean square error (RMSE) and mean absolute error (MAE) by an average of 48.57% and 47.72%, respectively.

Whistle, whine and modal implications due to the electromechanical coupling of an electric powertrain

Electric (e-) powertrain Noise, Vibration and Harshness (NVH) shows tonal behaviour due to the electromagnetic (EM) forces in the e-motor (leading to whistling noise) and the gear meshing (leading to whining noise). Tonal excitations activate different system modes as the motor speed changes, amplifying whistling and whining. Accurate prediction of resonance speeds and aggressive NVH is essential. Due to EM interactions, the e-motor exhibits EM torsional stiffness, affecting the system dynamics. Past studies considered EM stiffness in torsional vibration lumped parameter models, mostly neglecting flexible components (i.e. powertrain housing). In this work, the EM stiffness is calculated using the Frequency Response Function method and is included for the first time in the modal analysis of a three-dimensional e-powertrain model. This enables e-powertrain electromechanical coupling analysis during the design phase, considering the housing three-dimensional flexibility, a key NVH contributor. Conducting modal analysis with the EM stiffness shows effects on the system natural frequencies, along with the mode shapes at different frequency ranges and operating speeds, establishing an accurate design method to describe the e-powertrain dynamics and provide clearer insight on the NVH physics.

Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains

The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants of the conventional SST k-ω model and the linear Reynolds stress model (RSM) featuring optimized closure constants. Then, these two modified models and their origin models are applied to compare and analyze wind flows from a 3D hill wind tunnel experiment and two field measurements over typical complex terrain, including Askervein hill and Bolund island, with the aim of analyzing the sensitivity of wind flows to different RANS turbulence models. The study focuses on analyzing the effects of different turbulence models on the self-sustainability of wind speed and turbulent kinetic energy upstream of the computational domain and on the accuracy of wind flow prediction over complex terrain. The results show that our modified RSM model shows better agreement with the available experimental data on the upstream and leeward sides of all simulated hills. The wind speed on the leeward slope is particularly sensitive to the turbulence model, with a maximum difference in the relative root mean square error (RRMSE) that can reach 11% among the four models. The accuracy of the turbulent kinetic energy depends on the self-sustainability of the upstream turbulent kinetic energy and the predictive ability of the turbulence model for separated flows, and the maximum difference in the RRMSE of the four models can reach 47%. In addition, the advantages and disadvantages of the tested models are discussed to provide guidance for model selection during wind flow simulations in complex terrain.