AUTHOR=Sun Zhenao , Shen Yongshan , Chen Zhe , Teng Yun , Qian Xiaoyi 

TITLE=Interval Prediction Method for Wind Speed Based on ARQEA Optimized by Beta Distribution and SWLSTM

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.927260

DOI=10.3389/fenrg.2022.927260

ISSN=2296-598X

ABSTRACT=The interval prediction of wind speed is crucial for the economic and safe operation of wind farms. To overcome the probability density function parameter optimization and long-term correlation of time series problems in an interval prediction method, a hybrid model based on the beta distribution of an allele real-coded quantum evolutionary algorithm (ARQEA) and a shared weight long short-term memory neural network (SWLSTM) is proposed for predicting the interval of short-term wind speed, which is beta-ARQEA-SWLSTM. Input variables are determined via autocorrelation functions, and the shape and position parameters in the beta distribution function are optimized by the ARQEA algorithm. An interval-divided multi-distribution function aggregation is proposed to deal with the fluctuation of wind speed series. Lastly, case studies are provided to demonstrate the effectiveness of the proposed method.