山东电力技术2024，Vol.51Issue(4)：64-71,8.DOI:10.20097/j.cnki.issn1007-9904.2024.04.007
基于猎人猎物优化算法优化BiLSTM的电力负荷短期预测
Short-term Load Prediction Based on BiLSTM Optimized by Hunter-prey Optimization Algorithm
摘要
Abstract
A short-term prediction model of power load based on bidirectional long short-term memory(BiLSTM)optimized by empirical mode decomposition(EMD)and hunter-prey optimization(HPO)algorithm was proposed,which can solve the problem that the prediction accuracy of power load is not high due to randomness and nonlinearity.Firstly,the load time series was decomposed into multiple intrinsic mode function components and a residual component by empirical mode decomposition.Then,the hunter-prey optimization algorithm was used to optimize the bidirectional long-term and short-term memory neural network to construct the HPO-BiLSTM prediction model.And each intrinsic mode function component and residual component were normalized and input into the HPO-BiLSTM prediction model for prediction.The predicted values of each component were inversely normalized and directly added to obtain the final prediction result.Finally,the load data of a certain area from March 1 to 11,2018 were selected for analysis.The simulation results show that compared with BiLSTM,HPO-BiLSTM,EMD-BiLSTM,EMD-GA-BiLSTM and EMD-PSO-BiLSTM prediction models,the EMD-HPO-BiLSTM prediction model has higher prediction accuracy and better fitting effect.关键词
双向长短期记忆神经网络/猎人猎物优化算法/电力负荷/短期预测/经验模态分解Key words
bidirectional long short-term memory/hunter-prey optimization algorithm/load/short-term forecast/empirical mode decomposition分类
信息技术与安全科学引用本文复制引用
陈晓华,吴杰康,蔡锦健,唐文浩,龙泳丞,王志平..基于猎人猎物优化算法优化BiLSTM的电力负荷短期预测[J].山东电力技术,2024,51(4):64-71,8.基金项目
国家自然科学基金项目(50767001). National Natural Science Foundation of China(50767001). （50767001）
