基于改进PSO优化LSTM网络的短期电力负荷预测

doi:10.16182/j.issn1004731x.joss.20-0297

摘要/Abstract

摘要： 为了提高短期电力负荷预测的精度,提出一种基于自适应柯西变异粒子群(ACMPSO)算法优化长短期记忆(LSTM)神经网络的短期电力负荷预测模型(ACMPSO-LSTM)。针对LSTM模型参数较难选取的问题,采用ACMPSO算法进行LSTM模型参数寻优,利用非线性变化惯性权重来提高PSO算法的全局寻优能力和收敛速度,并在寻优过程中添加了基于遗传算法中的变异操作,减小粒子陷入局部最优解的风险。仿真结果表明,ACMPSO优化LSTM的方法能够有效提高短期电力负荷预测的精度和稳定性。

关键词: 短期电力负荷预测, 粒子群算法, 长短期记忆神经网络, 惯性权重, 变异操作

Abstract: To improve the accuracy of short-term power load forecasting, a short-term power load forecasting model (ACMPSO-LSTM) based on long-short memory neural network (LSTM) optimized by adaptive Cauchy mutation particle swarm optimization (ACMPSO) is proposed. For the problem of difficult selection of LSTM model parameters, ACMPSO is used to optimize model parameters, and non-linear changing inertia weights are adopted to improve the global optimization ability and convergence speed of PSO algorithm. In the optimization process, a mutation operation based on genetic algorithm is added to reduce the risk of particles falling into local optimal solutions. The simulation results show that the ACMPSO algorithm for LSTM can effectively improve the accuracy and stability of short-term power load forecasting.

Key words: short-term power load forecasting, particle swarm optimization, long short term memory neural network, inertia weight, mutation operation

中图分类号:

TP391.9

魏腾飞, 潘庭龙. 基于改进PSO优化LSTM网络的短期电力负荷预测[J]. 系统仿真学报, 2021, 33(8): 1866-1874.

Wei Tengfei, Pan Tinglong. Short-term Power Load Forecasting Based on LSTM Neural Network Optimized by Improved PSO[J]. Journal of System Simulation, 2021, 33(8): 1866-1874.

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