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

doi:10.1038/s41598-025-98212-8

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

2025-05-08

0

OA

AI

DOI：10.1038/s41598-025-98212-8

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Long short term memory

Renewable energy

Data-transformation

Deep recurrent neural network

Feature selection

Wind turbine power

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

Abstract

Journal

Organization