Year: 2024
Type: article
Abstract: Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques.
Source: Heliyon
Institutions: National Yunlin University of Science and Technology, Imam Mohammad ibn Saud Islamic University, Taiz University, Urmia University, Asia University, Symbiosis International University, University of Petroleum and Energy Studies, National Pingtung University, National University of Malaysia, Obuda University, Ludovika University of Public Service
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,100
Awards: Developing a Mobile Platform for Big Data Analysis in Adaptive Education: Utilizing Large Language Models and Explainable Artificial Intelligence to (1) Monitor Real-Time Learning Dynamics, (2) Facilitate Personalized Education, and (3) Eliminate Disparities and Biases in Assessments, Innovative Approaches for Air Pollution Forecasting: Hybrid Transfer and Deep Learning Models, G6295576664
OpenAlex ID: W4405336977