基于CNN-BiLSTM与DTW的非侵入式住宅负荷监测方法

Non-intrusive Residential Load Monitoring Method Based on CNN-BiLSTM and DTW

为减少居民生活用电浪费现象,非侵入式负荷监测技术显示出其重要性。基于事件检测的情况下,该文提出一种基于卷积神经网络耦合双向长短时记忆神经网络(convolutionalneuralnetworksandBi-directionallong short-term memory,CNN-BiLSTM)与动态时间规划(dynamic time warping,DTW)的非侵入式住宅负荷监测方法。首先通过概率质量函数计量负荷的运行状态信息,提取出稳态运行时的U-I特性曲线图;然后将图片归一化为统一格式的灰度图,利用卷积神经网络提取出特征向量作为负荷印记;将其输入至双向长短时记忆神经网络中进行辨识,并利用动态时间规划算法优化辨识结果,实现高精度的负荷辨识。最后,利用PLAID公开数据集对于所提算法进行仿真验证,实验证明所选负荷印记具有良好的辨识性能,辨识算法相比对比算法具有更高的信度与准确率。

To reduce the waste of electricity in residents’lives,non-intrusive load monitoring shows its importance.Under the premise of event detection,a non-invasive residential load monitoring method based on convolutional neural networks-Bi-directional long short-term memory(CNN-BiLSTM)and dynamic time warping(DTW)is proposed.Firstly,the information of load operation state is measured by probability mass function,and the U-I characteristic curve of steady-state operation is extracted;Then,the image is normalized to a unified gray scale image,and the feature vector is extracted by convolution neural network as the load signature;Input the data into the BiLSTM for identification and use the DTW to optimize the identification results to achieve high identification accuracy.Finally,the PLAID public data set is used to simulate and verify the proposed algorithm.The simulation results show that the selected load signature has good identification performance,and the identification algorithm has higher reliability and accuracy than the comparison algorithm.