Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition

Energy, Journal Year: 2024, Volume and Issue: 292, P. 130493 - 130493

Published: Jan. 27, 2024

Language: Английский

---

Fast fluid–structure interaction simulation method based on deep learning flow field modeling

Physics of Fluids, Journal Year: 2024, Volume and Issue: 36(4)

Published: April 1, 2024

The rapid acquisition of high-fidelity flow field information is great significance for engineering applications such as multi-field coupling. Current research in modeling predominantly focuses on low Reynolds numbers and periodic flows, exhibiting weak generalization capabilities notable issues with temporal inferring error accumulation. Therefore, we establish a reduced order model (ROM) based Convolutional Auto-Encoder (CAE) Long Short-Term Memory (LSTM) neural network propose an unsteady method the airfoil high number strong nonlinear characteristics. attention mechanism physical constraints are integrated into architecture to improve accuracy. A broadband excitation training strategy proposed overcome accumulation problem long-term inferring. With only small amount latent codes, relative reconstructed by CAE can be less than 5‰. By LSTM signals, stable dynamic evolution achieved time dimension. CAE-LSTM accurately predict forced response complex limit cycle behavior wide range amplitude frequency under subsonic/transonic conditions. errors predicted variables integral force 1%. fluid–structure interaction framework built coupling ROM motion equations structure. predicts series pitch displacement moment coefficient at different frequencies, which good agreement computational fluid dynamics, simulation savings exceed one magnitude.

Language: Английский

---

A long sequence time-series forecasting model for ship motion attitude based on informer

Ocean Engineering, Journal Year: 2024, Volume and Issue: 305, P. 117861 - 117861

Published: April 23, 2024

Language: Английский

---

Intelligent prediction algorithm of ship roll and pitch motion based on SSA- optimized BiLSTM network

Ocean Engineering, Journal Year: 2025, Volume and Issue: 320, P. 120331 - 120331

Published: Jan. 13, 2025

Language: Английский

---

A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 580 - 580

Published: Jan. 26, 2025

Accurate wind power forecasting is crucial for optimizing grid scheduling and improving utilization. However, real-world time series exhibit dynamic statistical properties, such as changing mean variance over time, which make it difficult models to apply observed patterns from the past future. Additionally, execution speed high computational resource demands of complex prediction them deploy on edge computing nodes farms. To address these issues, this paper explores potential linear constructs NFLM, a linear, lightweight, short-term model that more adapted characteristics data. The captures both long-term sequence variations through continuous interval sampling. mitigate interference features, we propose normalization feature learning block (NFLBlock) core component NFLM processing sequences. This module normalizes input data uses stacked multilayer perceptron extract cross-temporal cross-dimensional dependencies. Experiments with two real farms in Guangxi, China, showed compared other advanced methods, MSE 24-step ahead respectively reduced by 23.88% 21.03%, floating-point operations (FLOPs) parameter count only require 36.366 M 0.59 M, respectively. results show can achieve good accuracy fewer resources.

Language: Английский

---

Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 652 - 652

Published: Jan. 30, 2025

Accurate prediction of regional wind power generation intervals is an effective support tool for the economic and stable operation provincial grid. However, it involves a large amount high-dimensional meteorological historical information related to massive stations in province. In this paper, lightweight model developed directly obtain probabilistic predictions form intervals. Firstly, input features are formed through fused image method geographic as well aggregation strategy, which avoids extensive tedious data processing process prior modeling traditional approach. Then, order effectively consider spatial distribution characteristics temporal power, parallel network architecture convolutional neural (CNN) long short-term memory (LSTM) designed. Meanwhile, efficient channel attention (ECA) mechanism improved quantile regression-based loss function introduced training generate The case study shows that proposed paper improves interval performance by at least 12.3% reduces deterministic root mean square error (RMSE) 19.4% relative benchmark model.

Language: Английский

---

Network Traffic Prediction Model Based on Convolutional Neural Networks‐Long Short‐Term Memory and iTransformer

Internet Technology Letters, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

ABSTRACT Accurately predicting network traffic is crucial for dynamically deploying computing resources in data centers and reducing carbon emissions. In this paper, a hybrid prediction model Convolutional Neural Networks‐Long Short‐Term iTransformer (CNN‐LSTM‐iTransformer) based on CNN‐LSTM proposed. CNN—LSTM used to capture local features long—term dependencies, while employed feature relevance learning prediction. addition, the Huber Loss function further improve accuracy. experiment, dataset was provided by Ant Financial Group, experimental results show that CNN—LSTM—iTransformer significantly reduces MAE 0.112, MSE 0.0212, MAPE 0.123, RWMAPE which represents risk 0.122, so CNN‐LSTM‐iTransformer achieves not only higher accuracy but also lower risk.

Language: Английский

---

An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division

Energy, Journal Year: 2024, Volume and Issue: 299, P. 131383 - 131383

Published: April 25, 2024

Language: Английский

---

Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting

Energy, Journal Year: 2024, Volume and Issue: 304, P. 131966 - 131966

Published: June 12, 2024

Language: Английский

---

Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks

Applied Energy, Journal Year: 2024, Volume and Issue: 376, P. 124308 - 124308

Published: Aug. 27, 2024

Language: Английский

---