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

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 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.

Язык: Английский

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

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 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.

Язык: Английский

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