实时解聚分析家庭能源消耗事件的基础设施仲裁传感新方法  

New method of real-time disaggregation and analysis of residential resource consumption events using infrastructure-mediated sensing

作　　者：周求湛[1] 胡继康[1] 刘萍萍[2] 车遥[1] 陈永志[1] 

机构地区：[1]吉林大学通信工程学院,长春130022 [2]吉林大学计算机科学与技术学院,长春130012

出　　处：《吉林大学学报（工学版）》2014年第2期516-524,共9页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(60906034);吉林大学基本科研业务费项目(60481080)

摘　　要：提出了一种全新的根据家庭中实时水电能源消耗事件的分析方法。首先设计了一套城市居民水电能源消耗的通用感知系统,采用基础设施仲裁传感技术,获取家庭中的水电消耗数据。其次,采用功率谱向量时间追赶算法和最近邻居分类算法,依据开关电源设备向电力线辐射的高频电磁干扰特性,实现了对用电设备的快速识别和分类;采用滑动窗技术和基于向量内积的模式识别方法,可以快速分类出用水设备。通过在实际家庭中的验证,本方法达到了95%的设备识别和分类精度。最后,可将分析和处理的结果通过以太网上传到云存储端。家庭水电能源消耗的解聚分析是完成对家庭能源的监控和居民行为分析的基础。In order to analyze human activities accurately, a new analysis approach is proposed combining with real-time water and electrical events. First, applying infrastructure-mediated sensing technology, a system of sensing residential water and power consumption is designed to obtain the water and electrical usage data. Then, using the power spectrum vector chasing algorithm and k-Nearest Neighbor algorithm, working devices in the home can be recognized and classified according to the abundant of high electromagnetic interference noises generated during the switching of power supply devices. The fixtures in the home can be classified using sliding window technique and pattern recognition algorithm based on the inner product of vector. Via being deployed in real-houses, this approach successfully classifies consumption events in device-level with 95% accuracy. Finally, the analysis and post-process results can be transmitted to a clouding storage via internet. Disaggregated residential water and power consumption is the fundamental of energy monitoring and human activity analysis in a home.

关 键 词：信息处理技术 居民行为分析 水电事件感知 能量监控 基础设施仲裁传感 

分 类 号：TN911.6[电子电信—通信与信息系统]