Iterative signal separation assisted energy disaggregation
Iterative signal separation assisted energy disaggregation
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DOI：
10.1109/igcc.2015.7393701
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
Nilavra Pathak;Nirmalya Roy;A. Biswas
中科院分区：
文献类型：
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作者：
Nilavra Pathak;Nirmalya Roy;A. Biswas
在公用事业法案中提供逐项耗尽的能源正在成为优先事项，也许是在短期内成为业务实践。最近，已经开发了许多系统，例如智能插头，智能断路器等，用于非侵入性负载监控（NILM）。它们与智能电表或插件级集成到整个家庭环境中的足迹级别的能耗模式，同时在一个家庭中部署现有技术是可行的，可以在一个地区的成千上万的家庭中扩展这些技术的进步尚未实现。这主要是由于与这些类型的实际部署相关的成本，部署复杂性和侵入性。在这些缺点的推动下，在本文中，我们通过提出一种在大型数据集上工作以准确解构累积信号来解决可扩展分解的第一步，以解决可扩展的分解。我们提出了一种基于迭代噪声分离的方法，使用基于稀疏编码的方法进行能量分解，该方法在房屋的单个入口点（即在仪表级别）起作用。我们进行了一项排名的迭代信号去除方法，该方法有效地将电器的单个信号波形作为中等粒度（1分钟）的聚集能量数据集上的噪声分离为噪声。我们在实际数据集上进行了实验，并获得了大约94％的能量分解，即分解设备的信号估计精度。
Providing itemized energy consumption in a utility bill is becoming a priority, and perhaps a business practice in the near term. In recent times, a multitude of systems have been developed such as smart plugs, smart circuit breakers etc., for non-intrusive load monitoring (NILM). They are integrated either with the smart meters or at the plug-levels to footprint appliance-level energy consumption patterns in an entire home environment While deploying the existing technologies in a single home is feasible, scaling these technological advancements across thousands of homes in a region is not realized yet. This is primarily due to the cost, deployment complexity, and intrusive nature associated with these types of real deployment. Motivated by these shortcomings, in this paper we investigate the first step to address scalable disaggregation by proposing a disaggregation mechanism that works on a large dataset to accurately deconstruct the cumulative signals. We propose an iterative noise separation based approach to perform energy disaggregation using sparse coding based methodologies which work at the single ingress point of a home, i.e., at the meter level. We performed a ranked iterative signal removal methodology that effectively isolates appliances' individual signal waveform as noise on an aggregate energy datasets with moderate granularity (1 min). We performed experiments on real dataset and obtained approximately 94% energy disaggregation, i.e., disaggregated appliance-wise signal estimation accuracy.