电测与仪表2024，Vol.61Issue(1)：131-136,6.DOI:10.19753/j.issn1001-1390.2024.01.020
基于改进粒子群算法优化LSTM的短期电力负荷预测
The short-term power load forecasting based on NIWPSO-LSTM neural network
摘要
Abstract
Power load data has time-sequence and non-linear characteristics,and long short-term memory(LSTM)neural network can handle the above data characteristics.However,the performance of the LSTM algorithm has a great dependence on the preset parameters,and the parameters set by experience will make the model have low generalization performanceand reduce the prediction effect.In order to solve the above problems,this paper proposes a prediction model NIWPSO-LSTM combining the nonlinear dynamic inertia weight particle swarm optimization(NIWPSO)and long-short-time memory(LSTM)neural network.The nonlinear dynamic inertial weights are used to improve the global optimization ability of PSO,and then,the key parameters of LSTM are optimized through NIWPSO.The experimental results show that the prediction ac-curacy of NIWPSO-LSTM is much higher than other models,which verifies the feasibility of the proposed scheme.关键词
短期电力负荷预测/机器学习/非线性动态调整惯性权重粒子群算法/LSTMKey words
short-term power load forecasting/machine learning/NIWPSO/LSTM neural network分类
信息技术与安全科学引用本文复制引用
崔星,李晋国,张照贝,李麟容..基于改进粒子群算法优化LSTM的短期电力负荷预测[J].电测与仪表,2024,61(1):131-136,6.基金项目
国家自然科学基金资助项目(61702321) （61702321）
国家自然科学基金资助项目(U1936213) （U1936213）
