Enhanced framework embedded with data transformation and multi-objective feature selection algorithm for forecasting wind power DOI Creative Commons
Yahya Z. Alharthi, Haruna Chiroma, Lubna Abdelkareim Gabralla

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 8, 2025

The increasing global interest in utilizing wind turbines for power generation emphasizes the importance of accurate forecasting managing power. This paper proposed a framework that integrates data transformation mechanism with multi-objective none-dominated sorting genetic algorithm III (NSGA-III), coupled hybrid deep Recurrent Network (DRN) and Long Short-Term Memory (LSTM) architecture modeling feature selection algorithm, NSGA-III, identifies optimal subset features from energy datasets. These selected undergo process before being input into DRN-LSTM forecasting. A comparative study demonstrates proposal's superior effectiveness robustness compared to existing frameworks proposal achieving 2.6593e-10 1.630e-05 terms MSE RMSE respectively whereas classical recorded 8.8814e-07 9.424e-04. study's contributions lie its approach integration notable enhancements accuracy. Furthermore, offers valuable insights guide research efforts future.

Language: Английский

Pseudo-Twin Neural Network of Full Multi-Layer Perceptron for Ultra-Short-Term Wind Power Forecasting DOI Open Access

Yulong Yang,

Jiaqi Wang, Baihui Chen

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(5), P. 887 - 887

Published: Feb. 24, 2025

In recent wind power forecasting studies, deep neural networks have shown powerful performance in estimating future from data. this paper, a pseudo-twin network model of full multi-layer perceptron is proposed for farms. model, the input data are divided into physical attribute and historical These two types processed separately by feature extraction module structure to obtain features features. To ensure comprehensive integration establish connection between extracted features, mixing introduced cross-mix After mixing, set perceptrons used as regression forecast power. simulation research carried out based on measured The compared with mainstream models such CNN, RNN, LSTM, GRU, hybrid network. results show that MAE RMSE single-step reduced up 21.88% 16.85%, respectively. Additionally, 1 h rolling (six steps ahead) 31.58% 40.92%,

Language: Английский

Citations

1
A novel fractional order grey Euler model and its application in China's clean energy production prediction DOI

Zhongsen Yang,

Yong Wang, Neng Fan

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135609 - 135609

Published: March 1, 2025

Language: Английский

Citations

0
Darvfl-Lstm: A Time Series Prediction Model Integrating Dynamic Regularization and Attention Mechanism DOI
Gaofeng Liu

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhanced framework embedded with data transformation and multi-objective feature selection algorithm for forecasting wind power DOI Creative Commons
Yahya Z. Alharthi, Haruna Chiroma, Lubna Abdelkareim Gabralla

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 8, 2025

The increasing global interest in utilizing wind turbines for power generation emphasizes the importance of accurate forecasting managing power. This paper proposed a framework that integrates data transformation mechanism with multi-objective none-dominated sorting genetic algorithm III (NSGA-III), coupled hybrid deep Recurrent Network (DRN) and Long Short-Term Memory (LSTM) architecture modeling feature selection algorithm, NSGA-III, identifies optimal subset features from energy datasets. These selected undergo process before being input into DRN-LSTM forecasting. A comparative study demonstrates proposal's superior effectiveness robustness compared to existing frameworks proposal achieving 2.6593e-10 1.630e-05 terms MSE RMSE respectively whereas classical recorded 8.8814e-07 9.424e-04. study's contributions lie its approach integration notable enhancements accuracy. Furthermore, offers valuable insights guide research efforts future.

Language: Английский

Citations

0