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

作　　者：陈晓华 吴杰康[2] 蔡锦健 唐文浩 龙泳丞 王志平 CHEN Xiaohua;WU Jiekang;CAI Jinjian;TANG Wenhao;LONG Yongcheng;WANG Zhiping(Zhanjiang Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Zhanjiang 524005,China;School of Automation,Guangdong University of Technology,Guangzhou 510006,China;School of Electrical Engineering&Intelligentization,Dongguan University of Technology,Dongguan 523808,China)

机构地区：[1]广东电网有限责任公司湛江供电局,广东湛江524005 [2]广东工业大学自动化学院,广东广州510006 [3]东莞理工学院电子工程与智能化学院,广东东莞523808

出　　处：《山东电力技术》2024年第4期64-71,共8页Shandong Electric Power

基　　金：国家自然科学基金项目(50767001)。

摘　　要：针对电力负荷由于随机性和非线性等原因导致预测精度不高等问题,基于经验模态分解(empirical mode decomposition,EMD)与猎人猎物优化(hunter-prey optimization,HPO)算法,提出一种优化双向长短期记忆神经网络(bidirectional long short-term memory,BiLSTM)的电力负荷短期预测模型。首先,利用经验模态分解将电力负荷时间序列分解成多个固有模态函数分量和一个残差分量。然后,利用猎人猎物优化算法优化双向长短期记忆神经网络构建HPOBiLSTM预测模型,并将各个固有模态函数分量和残差分量归一化后输入HPO-BiLSTM预测模型中进行预测,把各分量的预测值反归一化后直接相加得到最终的预测结果。最后,选取某地区在2018年3月1日-11日的电力负荷数据进行分析,仿真结果表明,与BiLSTM、HPO-BiLSTM、EMD-BiLSTM、EMD-GA-BiLSTM和EMD-PSO-BiLSTM预测模型相比,EMD-HPO-BiLSTM预测模型的预测精度更高,拟合效果更好。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 

分 类 号：TM714[电气工程—电力系统及自动化]