基于LSTM深度网络的配变负荷预测及调整研究  

Research on Load Prediction and Adjustment of Distribution Substation Based on LSTM Deep Networks

作　　者：马妍 孔汉杰 赵建 郭岩岩 李东阳 MA Yan;KONG Hanjie;ZHAO Jian;GUO Yanyan;LI Dongyang(Zhengzhou Power Supply Company,State Grid Henan Electric Power Company,Zhengzhou 450052,China)

机构地区：[1]国网河南省电力公司郑州供电公司,河南郑州450052

出　　处：《电工技术》2025年第5期40-43,47,共5页Electric Engineering

摘　　要：随着可再生能源,如光伏、风能和太阳能的广泛接入电网,电力公司亟需实施精确的短期负荷预测,以确保电网的稳定运行。采用了数据分解技术来消除负荷数据中的噪声和随机干扰,引入变分模态分解(VMD)算法来将原始负荷序列分解为不同频率的简单子序列。基于这些子序列,提出了一种结合VMD和改进CNN-LSTM的组合预测方法。实例分析表明,VMD-DA-RCLSTM模型的RMSE、MAPE、MAE指标均有所降低,说明所提组合预测模型有助于提高电力负荷预测的准确性。With the widespread access of renewable energy sources such as photovoltaic,wind,and solar to the grid,there is an urgent need for electric utilities to implement accurate short-term load forecasting to ensure the stable operation of the grid.In this paper,a data decomposition technique is employed to eliminate noise and random disturbances in the load data,and a variational modal decomposition(VMD)algorithm is introduced to decompose the original load sequence into simple subsequences of different frequencies.Based on these subsequences,a combined prediction method combining VMD and improved CNN-LSTM is proposed in this paper.From the example analysis,it is shown that the RMSE,MAPE,and MAE indexes of the VMD-DA-RCLSTM model are reduced,which indicates that the proposed combined forecasting model helps to improve the accuracy of power load forecasting.

关 键 词：短期负荷预测 模态分解 长短时记忆神经网络 组合模型 

分 类 号：TM715[电气工程—电力系统及自动化]