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

ABSTRACT: 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

SUBMITTER: Alharthi YZ 

PROVIDER: S-EPMC12062497 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature