电力系统保护与控制2024，Vol.52Issue(8)：123-133,11.DOI:10.19783/j.cnki.pspc.231402
基于DBO-VMD和IWOA-BILSTM神经网络组合模型的短期电力负荷预测
Short-term power load prediction based on DBO-VMD and an IWOA-BILSTM neural network combination model
摘要
Abstract
The share of renewable energy in modern power systems is increasing,causing its load to fluctuate more erratically than in conventional power systems.This volatility leads to lower accuracy of load prediction.To address this issue,this paper introduces a short-term load prediction model combining the dung beetle optimization algorithm(DBO)with optimized variational mode decomposition(VMD)and an improved whale optimization algorithm to optimize bidirectional long short-term memory(IWOA-BILSTM)neural networks.The DBO is used to optimize the VMD,the time series data is decomposed,and various feature data are classified according to the minimum envelope entropy.This enhances the decomposition effect.The fluctuation of the data is reduced by effectively decomposing the original data.Then the whale optimization algorithm is improved using a nonlinear convergence factor,adaptive weight strategy and random difference variation strategy to enhance the local and global search ability of the whale optimization algorithm.Thus an improved whale optimization algorithm(IWOA)is obtained,and it is then used to optimize bidirectional long short-term memory(BILSTM)neural networks,increasing the accuracy of model predictions.Finally,this method is tested on real load data from a location,yielding favorable results.The resulting metrics for relative root mean square,mean absolute and mean absolute percentage errors are recorded at 0.0084,48.09,and 0.66%,respectively.These outcomes verify the effectiveness of the proposed model in short-term load prediction.关键词
蜣螂优化算法/VMD/改进鲸鱼算法/短期电力负荷预测/双向长短期记忆神经网络/组合算法Key words
dung beetle optimization(DBO)algorithm/VMD/improved whale algorithm/short-term electric load prediction/bidirectional long and short-term memory neural networks(BILSTM)/combinatorial algorithms引用本文复制引用
刘杰,从兰美,夏远洋,潘广源,赵汉超,韩子月..基于DBO-VMD和IWOA-BILSTM神经网络组合模型的短期电力负荷预测[J].电力系统保护与控制,2024,52(8):123-133,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.62103177). 国家自然科学基金项目资助(62103177) （No.62103177）
