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

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Enhanced framework embedded with data transformation and multi-objective feature selection algorithm for forecasting wind power

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月8日

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  • Yahya Z. Alharthi,
  • Haruna Chiroma,
  • Lubna A. Gabralla

摘要

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