An adaptive prediction method based on VMD-GRU for future driving condition of vehicle DOI
Yong Chen,

Zhongda Song,

Yanmin Huang

et al.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

Driving conditions prediction plays an important role in energy-saving control strategy for electric vehicle. However, the complexity of changes road poses a great challenge to accurate driving condition. To address this problem, paper proposes adaptive Sliding Window (SW) and Gated Recurrent Unit (GRU) algorithm predict short period, enables adjust size SW promptly when change frequently. A smaller window is adopted case drastically changing speeds, larger smooth speeds. Firstly, Principal Component Analysis (PCA) k-means clustering are used construct sample with same characteristics. Then instantaneous frequency calculated by Hilbert transform Variational Mode Decomposition (VMD), optimal applicable different frequencies quantitatively calculated. The model provides precise predictions root mean square error (RMSE), absolute (MAE) percentage (MAPE) 0.8799, 0.5443 0.8362%, respective. ablation experiments show that improved GRU capture trends more accurately, improves accuracy robustness model.

Language: Английский

Medium-term offshore wind speed multi-step forecasting based on VMD and GRU-MATNet model DOI
Shibao Li, Liang Guo, Jun Zhu

et al.

Ocean Engineering, Journal Year: 2025, Volume and Issue: 325, P. 120737 - 120737

Published: March 3, 2025

Language: Английский

Citations

2
Combining Seasonal and Trend Decomposition Using LOESS With a Gated Recurrent Unit for Climate Time Series Forecasting DOI Creative Commons
Xiao Liu, Qianqian Zhang

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 85275 - 85290

Published: Jan. 1, 2024

Language: Английский

Citations

5
A synchronized multi-step wind speed prediction with adaptive features and parameters selection: Insights from an interaction model DOI
Wenxin Xia, Jinxing Che, Kun Hu

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124764 - 124764

Published: July 14, 2024

Language: Английский

Citations

5
Day-ahead load forecast based on Conv2D-GRU_SC aimed to adapt to steep changes in load DOI
Yunxiao Chen,

Chaojing Lin,

Yilan Zhang

et al.

Energy, Journal Year: 2024, Volume and Issue: 302, P. 131814 - 131814

Published: May 26, 2024

Language: Английский

Citations

4
Short-term offshore wind speed forecasting approach based on multi-stage decomposition and deep residual network with self-attention DOI
Hakan Açıkgöz, Deniz Korkmaz

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 146, P. 110313 - 110313

Published: Feb. 19, 2025

Language: Английский

Citations

0
Ultra-short-term wind speed hybrid forecasting model based on maximal information coefficient-optimized TVF-EMD and resTKAN DOI
Chenglin Yang, Wenyu Zhang, Jing Ren

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(4)

Published: March 24, 2025

Language: Английский

Citations

0
A Novel Hybrid Deep Learning Model for Day-Ahead Wind Power Interval Forecasting DOI Open Access

Jianjing Mao,

Jian Zhao, H. Zhang

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(7), P. 3239 - 3239

Published: April 5, 2025

Accurate interval forecasting of wind power is crucial for ensuring the safe, stable, and cost-effective operation grids. In this paper, we propose a hybrid deep learning model day-ahead forecasting. The begins by utilizing Gaussian mixture (GMM) to cluster daily data with similar distribution patterns. To optimize input features, feature selection (FS) method applied remove irrelevant data. empirical wavelet transform (EWT) then employed decompose both numerical weather prediction (NWP) into frequency components, effectively isolating high-frequency components that capture inherent randomness volatility A convolutional neural network (CNN) used extract spatial correlations meteorological while bidirectional gated recurrent unit (BiGRU) captures temporal dependencies within sequence. further enhance accuracy, multi-head self-attention mechanism (MHSAM) incorporated assign greater weight most influential elements. This leads development based on GMM-FS-EWT-CNN-BiGRU-MHSAM. proposed validated through comparison benchmark demonstrates superior performance. Furthermore, forecasts generated using NPKDE shows new achieves higher accuracy.

Language: Английский

Citations

0
Improving Offshore Wind Speed Forecasting with a CRGWAA-Enhanced Adaptive Neuro-Fuzzy Inference System DOI Creative Commons

Yingjie Liu,

Fahui Miao

Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(5), P. 908 - 908

Published: May 3, 2025

Accurate forecasting of offshore wind speed is crucial for the efficient operation and planning energy systems. However, inherently non-stationary highly volatile nature speed, coupled with sensitivity neural network-based models to parameter settings, poses significant challenges. To address these issues, this paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by CRGWAA. The proposed CRGWAA integrates Chebyshev mapping initialization, elite-guided reflection refinement operator, a generalized quadratic interpolation strategy enhance population diversity, adaptive exploration, local exploitation capabilities. performance comprehensively evaluated on CEC2022 benchmark function suite, where it demonstrates superior optimization accuracy, convergence robustness compared six state-of-the-art algorithms. Furthermore, ANFIS-CRGWAA model applied short-term using real-world data from region Fujian, China, at 10 m 100 above sea level. Experimental results show that consistently outperforms conventional hybrid baselines, achieving lower MAE, RMSE, MAPE, as well higher R2, across both altitudes. Specifically, original ANFIS-WAA model, RMSE reduced approximately 45% 24% m. These findings confirm effectiveness, stability, generalization ability complex, prediction tasks.

Language: Английский

Citations

0
A new framework for ultra-short-term electricity load forecasting model using IVMD–SGMD two–layer decomposition and INGO–BiLSTM–TPA–TCN DOI

Xiwen Cui,

Xiaodan Zhang, Dongxiao Niu

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 167, P. 112311 - 112311

Published: Oct. 10, 2024

Language: Английский

Citations

2
Data denoising and deep learning prediction for the wind speed based on NOA optimization DOI
Xinyi Xu, Shaojuan Ma, Cheng Huang

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Abstract Accurate short-term wind speed prediction is of great significance for power generation. Due to the insufficient traditional methods mine nonlinear features information, an improved time series method proposed by combining Variational Mode Decomposition (VMD) and Deep Learning (CNN-BiLSTM-AttNTS) with Nutcracker Optimization Algorithm (NOA). Firstly, NOA used optimize VMD CNN-BiLSTM, respectively. Secondly, we apply NOA-VMD decompose data into different Intrinsic Functions(IMFs). Then, phase space reconstruction (PSR) utilized identify chaotic characteristics components. Finally, NOA-CNN-BiLSTM-AttNTS model built up predict speed. Under same hyperparameters network structure settings, compared machine learning state-of-the-art hybrid models, results show that R-squared NOA-VMD-CNN-BiLSTM-AttNTS combination in this paper exceeds 90%, good accuracy generalization performance. The research result can provide reference guidance prediction.

Language: Английский

Citations

1