基于VMD-LSTM的非侵入式负荷识别方法

Non-intrusive load identification method based on VMD-LSTM

非侵入式负荷识别(Non-Intrusive Load Monitoring,NILM)技术仅基于家庭电源总入口处的电流、电压信息,获得室内电器设备的电气信息。提高负荷识别的精度,对于优化能源结构、提高电能利用效率、降低能耗、节约资源具有重要意义。首先应用变分模态分解(Variational Mode Decomposition,VMD)对归一化的电流信号分解为K个IMF分量,再估计各个分量与归一化电流信号的相关系数,挑选相关系数最大的两个分量作为负荷特征,输入训练好的LSTM神经网络进行识别。算例测试结果表明,该方法在公开数据集PLAID上的识别率高达99%,在实验室采集的数据集上的识别率为96.6%,证实了所提出方法对提升负荷识别精度有显著效果。

Non-intrusive load monitoring(NILM)technology is only based on the current and voltage information of the main en‐trance of home power supply to obtain the electrical information of indoor electrical equipment.Improving the accuracy of load identification is of great significance to optimize the energy structure,improve the efficiency of power utilization and reduce en‐ergy consumption.Firstly,the normalized current signal is decomposed by using variational mode decomposition(VMD),and then the correlation coefficients between each component and the original current signal are calculated.The two components with the largest correlation coefficients are selected as the load characteristics and input into the trained LSTM neural network for identification.The test results of an example show that the recognition rate of this method is up to 99%on public data set PLAID and 96.6%on laboratory data set,which proves the effectiveness of this method.