A novel hybrid model for building heat load forecasting based on multivariate Empirical modal decomposition DOI
Yiran Li,

Neng Zhu,

Yingzhen Hou

et al.

Building and Environment, Journal Year: 2023, Volume and Issue: 237, P. 110317 - 110317

Published: April 14, 2023

Language: Английский

A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction DOI

Jinlin Xiong,

Peng Tian,

Zihan Tao

et al.

Energy, Journal Year: 2022, Volume and Issue: 266, P. 126419 - 126419

Published: Dec. 13, 2022

Language: Английский

Citations

153
Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study DOI
Vikash Kumar Saini, Rajesh Kumar, Ameena Saad Al–Sumaiti

et al.

Electric Power Systems Research, Journal Year: 2023, Volume and Issue: 222, P. 109502 - 109502

Published: June 1, 2023

Language: Английский

Citations

46
Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method DOI
Irene Karijadi, Shuo‐Yan Chou, Anindhita Dewabharata

et al.

Renewable Energy, Journal Year: 2023, Volume and Issue: 218, P. 119357 - 119357

Published: Sept. 22, 2023

Language: Английский

Citations

43
A short-term wind power forecasting method based on multivariate signal decomposition and variable selection DOI
Ting Yang, Zhenning Yang, Fei Li

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 360, P. 122759 - 122759

Published: Feb. 6, 2024

Language: Английский

Citations

26
Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting DOI
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno

et al.

Applied Intelligence, Journal Year: 2024, Volume and Issue: 54(4), P. 3119 - 3134

Published: Feb. 1, 2024

Language: Английский

Citations

23
A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data DOI
Zhaohua Liu, Chang-Tong Wang, Hua‐Liang Wei

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 247, P. 123237 - 123237

Published: Jan. 19, 2024

Language: Английский

Citations

18
Wind power forecasting based on new hybrid model with TCN residual modification DOI Creative Commons

Jiaojiao Zhu,

Liancheng Su, Yingwei Li

et al.

Energy and AI, Journal Year: 2022, Volume and Issue: 10, P. 100199 - 100199

Published: Sept. 6, 2022

Wind energy has been widely utilized to alleviate the shortage of fossil resources. When wind power is integrated into grid on a large scale, grid's stability severely harmed due fluctuating and intermittent properties speed. Accurate forecasts help formulate good operational strategies for farms. A short-term forecasting method based new hybrid model proposed increase accuracy forecast. Firstly, time series are separated using complete ensemble empirical mode decomposition with adaptive noise obtain multiple components, which then predicted support vector regression machine optimized through search cross validation (GridSearchCV) algorithm. Secondly, residual modification temporal convolutional network constructed, variables high correlation selected as input features predict residuals power. Finally, prediction compared other models actual data farm demonstrate validity described method, results reveal that better performance. © 2017 Elsevier Inc. All rights reserved.

Language: Английский

Citations

48
SCADA system dataset exploration and machine learning based forecast for wind turbines DOI
Upma Singh, M. Rizwan

Results in Engineering, Journal Year: 2022, Volume and Issue: 16, P. 100640 - 100640

Published: Sept. 13, 2022

Language: Английский

Citations

41
Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction DOI
Guolian Hou, Junjie Wang, Yuzhen Fan

et al.

Energy, Journal Year: 2023, Volume and Issue: 286, P. 129640 - 129640

Published: Nov. 13, 2023

Language: Английский

Citations

38
Short-term wind power prediction based on modal reconstruction and CNN-BiLSTM DOI Creative Commons
Zheng Li,

Ruosi Xu,

Xiaorui Luo

et al.

Energy Reports, Journal Year: 2023, Volume and Issue: 9, P. 6449 - 6460

Published: June 16, 2023

Accurate prediction of short-term wind power plays an important role in the safe operation and economic dispatch grid. In response to current single algorithm that cannot further improve accuracy, this study proposes a combined model based on data processing, signal decomposition, deep learning. First, outliers original can affect accuracy. This detects by Z-score method fills them with cubic spline interpolation ensure integrity data. Second, for volatility power, time series is decomposed using complete ensemble empirical modal decomposition adaptive noise (CEEMDAN). The component complexity calculated sample entropy (SE), components are reconstructed according SE size Finally, traditional convolutional neural network (CNN) structure improved bi-directional long memory (BiLSTM) used extract feature links between superimpose results each obtain final value. experimental demonstrate hybrid proposed has better performance terms performance.

Language: Английский

Citations

24