电测与仪表2017，Vol.54Issue(11)：32-35,48,5.
基于人工蜂群优化极限学习机的短期负荷预测
Short-term load forecasting based on improved extreme learning machine with artificial bee colony algorithm
摘要
Abstract
Extreme learning machine (ELM) with random input weights and hidden biases may lead to unstable performance and low prediction accuracy.This paper proposes a new short-term load forecasting method based on artificial bee colony (ABC) algorithm and ELM (ABC-ELM).Firstly, historical load, meteorological factor and day of week are selected as input variables to build the ELM model.Secondly, optimal input weights and hidden biases of ELM are selected by ABC algorithm.Finally, the new model of load forecasting with optimized parameters is constructed based on ABC-ELM.The real load date from a large city in China is applied to estimate the performance of proposed method.Experiment results show that the new method has higher stability and accuracy than ELM and BP neural networks.关键词
短期负荷预测/极限学习机/人工蜂群Key words
short-term load forecasting/extreme learning machine/artificial bee colony分类
信息技术与安全科学引用本文复制引用
王文锦,戚佳金,王文婷,黄南天..基于人工蜂群优化极限学习机的短期负荷预测[J].电测与仪表,2017,54(11):32-35,48,5.基金项目
国家高技术研究发展计划(863计划)项目(SS2014AA052502) （863计划）
吉林省科技发展计划项目(20160411003XH) （20160411003XH）
吉林省社科基金(2015A2) （2015A2）
吉林省教育厅"十三五"科技项目(吉教科合字[2016]第90号) （吉教科合字[2016]第90号）
吉林市科技发展计划项目(20156407) （20156407）
