An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data
An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data
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DOI：
10.1109/tsg.2016.2631238
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发表时间：
2018-07-01
影响因子：
9.6
通讯作者：
Luo, Fengji
中科院分区：
文献类型：
--
作者：
Kong, Weicong;Dong, Zhao Yang;Luo, Fengji
预测级负荷模型对未来智能电网应用至关重要。与直接的电器监控方法不同，挖掘智能电表数据以非侵入性地生成设备级负载模型并推广到所有拥有智能电表的家庭更加灵活和方便。本文提出了一个全面的和可扩展的框架来解决居民家庭的负载分解问题。我们的方法研究了家用电器的建模为隐马尔可夫模型和解决的非侵入性负载监测的基础上分段整数二次约束规划分解到家电水平的家庭电源配置文件。给出了我们的方法与当前智能电表基础设施实现的结构，并基于公共数据集进行了模拟。所有数据都被下采样到与澳大利亚智能电表基础设施最低功能一致的速率。结果表明，我们的方法是能够与现有的智能电表生成设备级负荷模型，为其他智能电网的研究和应用。
Appliance-level load models are expected to be crucial to future smart grid applications. Unlike direct appliance monitoring approaches, it is more flexible and convenient to mine smart meter data to generate load models at device level nonintrusively and generalise to all households with smart meter ownership. This paper proposes a comprehensive and extensible framework to solve the load disaggregation problem for residential households. Our approach examines both the modelling of home appliances as hidden Markov models and the solving of non-intrusive load monitoring based on segmented integer quadratic constraint programming to disaggregate a household power profile into the appliance level. Structure of our approach to be implemented with current smart meter infrastructure is given and simulations are performed based on public datasets. All data are down-sampled to the rate that is consistent with the Australia smart meter infrastructure minimum functionality. The results demonstrate that our approach is able to work with existing smart meters to generate device level load model for other smart grid research and applications.