DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting

Energy, Journal Year: 2023, Volume and Issue: 286, P. 129588 - 129588

Published: Nov. 6, 2023

Language: Английский

---

An enhanced feature extraction based long short-term memory neural network for wind power forecasting via considering the missing data reconstruction

Energy Reports, Journal Year: 2023, Volume and Issue: 11, P. 97 - 114

Published: Nov. 27, 2023

Wind power forecasting plays a significant role in regulating the peak and frequency of system, which can improve wind receiving capacity. Despite plenty methods have been proposed to fortify accuracy forecasting, existing models do not consider reconstruction missing data extract spatiotemporal features from data. To address these issues, this study proposes an improved long short-term memory (LSTM) network based method reconstruct capture In order model, multiple imputation technique (MIT) is first developed fill up samples with reconstructed by analyzing correlation among variables raw Secondly, exploit spatial temporal reduce low computation complexity, new parallel convolutional involving dilated convolution causal established for extraction. Finally, further performance, LSTM applied long-term trends reveal internal relations derived features. The experimental results on benchmark dataset both demonstrate that obtain better performance.

Language: Английский

---

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Energies, Journal Year: 2024, Volume and Issue: 17(2), P. 384 - 384

Published: Jan. 12, 2024

High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation grids. The development artificial intelligence provides a new scheme modeling with strong correlation. In addition, existing models are mostly ‘black box’ models, lacking interpretability, which may lead to lack trust in model by grid dispatchers. Therefore, improving obtain interpretability has become an important challenge. this paper, interpretable short-term based on ensemble deep graph neural network designed. Firstly, (GNN) attention mechanism applied aggregate features data extracted, ability obtained. Then, long memory (LSTM) method used process extracted establish model. Finally, random sampling algorithm optimize hyperparameters improve learning rate performance Through multiple comparative experiments case analysis, results show that proposed higher accuracy than other traditional obtains reasonable time space dimensions.

Language: Английский

---

Wind forecasting-based model predictive control of generator, pitch, and yaw for output stabilisation – A 15-megawatt offshore

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 302, P. 118155 - 118155

Published: Feb. 1, 2024

As wind energy continuously expands its share in power generation, the grid has a higher requirement for stable production. This study aims forecasting-based turbine control to mitigate fluctuation caused by uncertainties. Firstly, compass-vector transformation supports model on direction forecasting besides velocity. Wind modelling adopts general network structure of learning-shaping learn transformed vector series. speed and averaged from prediction determine three-degree-of-freedom (3-DOF) reference as objective update system configuration. Subsequently, predictive (MPC) solves real-time regulation sparse quadratic programming (QP). Besides, loop integrates generator control, compensation, output buffer coordinate generator, pitch servo, yaw servo. According simulation, long short-term memory (LSTM) ensures mean accuracy over 0.997 30-s window. Its performance is more than dense (DNN), convolutional (CNN), CNN-LSTM. Compared baseline proposed MPC can reduce 7% oscillation 12% peak-to-peak. improves rotation stability 44% at high wind. The proven contribute better quality.

Language: Английский

---

Short-term wind power forecasting based on multi-scale receptive field-mixer and conditional mixture copula

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 112007 - 112007

Published: July 17, 2024

Language: Английский

---