Forecasting of wind speed under wind-fire coupling scenarios by combining HS-VMD and AM-LSTM DOI
Chuanying Lin, Xingdong Li,

Shi Tie-feng

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 77, P. 102270 - 102270

Published: Aug. 22, 2023

Language: Английский

An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer DOI Creative Commons
Yuqian Tian, Dazhi Wang, Guolin Zhou

et al.

Entropy, Journal Year: 2023, Volume and Issue: 25(4), P. 647 - 647

Published: April 12, 2023

Accurate wind power prediction can increase the utilization rate of generation and maintain stability system. At present, a large number studies are based on mean square error (MSE) loss function, which generates many errors when predicting original data with random fluctuation non-stationarity. Therefore, hybrid model for named IVMD-FE-Ad-Informer, is Informer an adaptive function combines improved variational mode decomposition (IVMD) fuzzy entropy (FE), proposed. Firstly, decomposed into K subsequences by IVMD, possess distinct frequency domain characteristics. Secondly, sub-series reconstructed new elements using FE. Then, robust Ad-Informer predicts predicted values each element superimposed to obtain final results power. Finally, analyzed evaluated two real datasets collected from farms in China Spain. The demonstrate that proposed superior other models performance accuracy different datasets, this effectively meet demand actual prediction.

Language: Английский

Citations

13
A contrastive learning-based framework for wind power forecast DOI
Nanyang Zhu,

Zemei Dai,

Ying Wang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 230, P. 120619 - 120619

Published: June 5, 2023

Language: Английский

Citations

13
Short-term wind speed forecasting based on a hybrid model of ICEEMDAN, MFE, LSTM and informer DOI Creative Commons
Xinxin Wang,

Shen Xiaopan,

AI Xue-yi

et al.

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(9), P. e0289161 - e0289161

Published: Sept. 8, 2023

Wind energy, as a kind of environmentally friendly renewable has attracted lot attention in recent decades. However, the security and stability power system is potentially affected by large-scale wind grid due to randomness intermittence speed. Therefore, accurate speed prediction conductive operation. A hybrid model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Multiscale Fuzzy Entropy (MFE), Long short-term memory (LSTM) INFORMER proposed this paper. Firstly, data are decomposed into multiple intrinsic mode functions (IMFs) ICEEMDAN. Then, MFE values each calculated, modes similar aggregated obtain new subsequences. Finally, subsequence predicted informer LSTM, sequence selects one better performance than two predictors, results superimposed final results. The also compared other seven related models four evaluation metrics under different periods verify its validity applicability. experimental indicate that ICEEMDAN, MFE, LSTM exhibits higher accuracy greater

Language: Английский

Citations

13
A fuzzy time series forecasting model with both accuracy and interpretability is used to forecast wind power DOI
Xinjie Shi, Jianzhou Wang, Bochen Zhang

et al.

Applied Energy, Journal Year: 2023, Volume and Issue: 353, P. 122015 - 122015

Published: Oct. 4, 2023

Language: Английский

Citations

13
Forecasting of wind speed under wind-fire coupling scenarios by combining HS-VMD and AM-LSTM DOI
Chuanying Lin, Xingdong Li,

Shi Tie-feng

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 77, P. 102270 - 102270

Published: Aug. 22, 2023

Language: Английский

Citations

12