Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy DOI
Linfei Yin, Yunzhi Wu

Applied Energy, Journal Year: 2021, Volume and Issue: 307, P. 118266 - 118266

Published: Dec. 3, 2021

Language: Английский

A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD DOI
Jiale Li,

Zihao Song,

Xuefei Wang

et al.

Energy, Journal Year: 2022, Volume and Issue: 251, P. 123848 - 123848

Published: April 4, 2022

Language: Английский

Citations

126
Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach DOI
Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno, Matheus Henrique Dal Molin Ribeiro

et al.

International Journal of Electrical Power & Energy Systems, Journal Year: 2022, Volume and Issue: 143, P. 108504 - 108504

Published: Aug. 2, 2022

Language: Английский

Citations

75
A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM DOI
Wenhui Liu, Yulong Bai,

xiaoxin Yue

et al.

Energy, Journal Year: 2024, Volume and Issue: 294, P. 130726 - 130726

Published: Feb. 16, 2024

Language: Английский

Citations

36
Wind power forecasting system with data enhancement and algorithm improvement DOI
Yagang Zhang,

Xue Kong,

Jingchao Wang

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 196, P. 114349 - 114349

Published: March 1, 2024

Language: Английский

Citations

20
A novel decomposition-ensemble prediction model for ultra-short-term wind speed DOI
Zhongda Tian, Hao Chen

Energy Conversion and Management, Journal Year: 2021, Volume and Issue: 248, P. 114775 - 114775

Published: Oct. 1, 2021

Language: Английский

Citations

101
Artificial Neural Networks Hidden Unit and Weight Connection Optimization by Quasi-Refection-Based Learning Artificial Bee Colony Algorithm DOI Creative Commons
Nebojša Bačanin, Timea Bezdan,

K. Venkatachalam

et al.

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 169135 - 169155

Published: Jan. 1, 2021

Artificial neural networks are one of the most commonly used methods in machine learning. Performance network highly depends on learning method. Traditional algorithms prone to be trapped local optima and have slow convergence. At other hand, nature-inspired optimization proven very efficient complex problems solving due derivative-free solutions. Addressing issues traditional algorithms, this study, an enhanced version artificial bee colony metaheuristics is proposed optimize connection weights hidden units networks. Proposed improved method incorporates quasi-reflection-based guided best solution bounded mechanisms original approach manages conquer its deficiencies. First, tested a recent challenging CEC 2017 benchmark function set, then applied for training five well-known medical datasets. Further, devised algorithm compared metaheuristics-based methods. The efficiency measured by metrics - accuracy, specificity, sensitivity, geometric mean, area under curve. Simulation results prove that outperforms terms accuracy convergence speed. improvement over different datasets between 0.03% 12.94%. quasi-refection-based mechanism significantly improves speed together with bounded, exploitation capability enhanced, which better accuracy.

Language: Английский

Citations

72
Hybridization of hybrid structures for time series forecasting: a review DOI
Zahra Hajirahimi, Mehdi Khashei

Artificial Intelligence Review, Journal Year: 2022, Volume and Issue: 56(2), P. 1201 - 1261

Published: May 16, 2022

Language: Английский

Citations

69
A novel framework to assess all-round performances of spatiotemporal fusion models DOI Creative Commons
Xiaolin Zhu, Wenfeng Zhan, Junxiong Zhou

et al.

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 274, P. 113002 - 113002

Published: March 23, 2022

Spatiotemporal data fusion, as a feasible and low-cost solution for producing time-series satellite images with both high spatial temporal resolution, has undergone rapid development over the past two decades more than one hundred spatiotemporal fusion methods developed. Accuracy assessment of fused is crucial users to select appropriate real-world applications. However, commonly used metrics do not comprehensively cover multiple aspects image quality, contain redundant information, are comparable across different study areas. To address these problems, this proposed novel framework assess all-round performances methods. Four accuracy metrics, including RMSE, AD, Edge, local binary patterns (LBP), were selected optimal set according criteria. These only quantify spectral information in but also greatly alleviate redundancy feature computational simplicity. Furthermore, inspired by Taylor diagrams, we designed an performance (APA) diagram provide visual tool comprehensive methods, supporting cross-comparison considering effects input land surface characteristics. The case three typical sites demonstrated that can better differentiate six This new promote guide suitable applications, well facilitate establishment standard procedure

Language: Английский

Citations

59
Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine DOI

Jujie Wang,

Quan Cui, Maolin He

et al.

Chaos Solitons & Fractals, Journal Year: 2022, Volume and Issue: 156, P. 111783 - 111783

Published: Jan. 14, 2022

Language: Английский

Citations

48
A novel decision support system for enhancing long-term forecast accuracy in virtual power plants using bidirectional long short-term memory networks DOI Creative Commons
Reza Nadimi, Mika Goto

Applied Energy, Journal Year: 2025, Volume and Issue: 382, P. 125273 - 125273

Published: Jan. 13, 2025

Language: Английский

Citations

2