AUTHOR=Wang Hairong 

TITLE=Extreme learning Kalman filter for short-term wind speed prediction

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1047381

DOI=10.3389/fenrg.2022.1047381

ISSN=2296-598X

ABSTRACT=Accurate prediction of wind speed is critical for realizing optimal operation for the wind farm in real time. The task of predicting wind speed is still challenging because wind speed has a nature of strong uncertainty. This article proposed a novel Extreme Learning Kalman Filter (ELKF) which integrates the sigma-point Kalman filter with extreme learning machine algorithm to accurately forecast the wind speed sequence using an Artificial Neural Network (ANN) based state-space model. In the proposed ELKF method, ANNs are used to construct the state equation of the state-space model. Sigma-point Kalman filter is used to address the recursive state estimation problem. Experimental data validations have been implemented to compare the proposed ELKF method with autoregressive (AR) and ANNs for short-term wind speed forecasting. The proposed ELKF shows better prediction performance.