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=10 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 (SWLSTM) neural network is proposed for predicting the interval of short-term wind speed, which is beta–ARQEA–SWLSTM. Input variables are determined