基于多模式集成深度融合的非侵入式负荷分解模型  

Non-intrusive Load Decomposition Model Based on Deep Fusion of Multi-modal Integration

作　　者：姚刚[1] 王赟 王远亮 宋子浩 YAO Gang;WANG Yun;WANG Yuanliang;SONG Zihao(College of Logistics Engineering,Shanghai Maritime University,Shanghai 201306,China;College of Electrical Engineering and Automation,East China Jiaotong University,Nanchang,Jiangxi 330013,China)

机构地区：[1]上海海事大学物流工程学院,上海201306 [2]华东交通大学电气与自动化学院,江西南昌330013

出　　处：《控制与信息技术》2023年第1期1-10,共10页CONTROL AND INFORMATION TECHNOLOGY

摘　　要：目前,基于深度学习的非侵入式负荷分解模型存在对长时间用电信息的时间依赖性建模能力受限及使用同一种分解模型对具有不同负荷特征的设备进行负荷分解所得到的分解误差达不到理想状态的问题。针对上述问题,文章提出了一种非侵入式负荷分解模型,其将基于CNN-LSTM-TPA的分解模型和改进的SVRVB-STCKF模型进行集成融合。首先,其使用时间模式注意力机制(TPA)加强CNN-LSTM分解模型对时间依赖性的建模能力,捕获原始用电信息的负荷特征并实现初步负荷分解;其次,采用支持向量机回归(SVR)建立目标设备的非线性状态空间模型,并利用改进的强跟踪技术和变分贝叶斯对容积卡尔曼滤波算法(CKF)进行改进,得到VB-STCKF模型,对初步分解结果进行二次动态调整。利用REDD和UKDALE公开数据集对所提模型进行验证,结果表明,所提加强模型的时间建模能力及其对初步分解结果进行动态调整的功能,可以有效降低模型的分解误差。In order to address the problems that the current non-intrusive load decomposition model based on deep learning has limited ability to model the time-dependence of long-time power consumption information, and load decomposition using the same decomposition model for devices with different load characteristics results in errors beyond the desired level. This paper proposes a non-intrusive load decomposition model, involving CNN-LSTM-TPA decomposition model and the improved SVR-VB-STCKF model. Firstly, the CNN-LSTM decomposition model was enhanced in the time-dependent modeling capacity by the temporal pattern attention(TPA) mechanism, to capture the load characteristics of the original electricity consumption information and conduct preliminary load decomposition. Secondly, the support vector regression(SVR) was used to model the nonlinear state space of the target device and the cubature Kalman filter(CKF) algorithm was modified by the improved tracking technology and variational Bayesian to create a VB-STCKF model for secondary dynamic adjustment to the preliminary decomposition results.Finally, the proposed model was verified with the public datasets(REDD and UKDALE). The verification results indicate the proposed enhanced time-dependent modeling capacity of model and dynamic adjustment to the preliminary decomposition results are obviously effective for reducing decomposition errors.

关 键 词：负荷分解 非侵入式负荷监测 节能减排 时间模式注意力机制 卷积神经网络 支持向量机回归 卡尔曼滤波 

分 类 号：TM933[电气工程—电力电子与电力传动]