Online load tracking is the problem of monitoring an individual electrical load’s energy usage by analyzing a building’s smart meter data. The problem is important, since many energy optimizations require fine-grained, per-load energy data in real time; it also differs from the well-studied problem of load disaggregation in that it emphasizes efficient, online operation and per-load accuracy, rather than accurate disaggregation of every building load via offline analysis. In essence, tracking a particular load creates a virtual power meter for it, which mimics having a networkedconnected power meter attached to it. To enable high performance, we take a model-driven approach that focuses on efficiently detecting a small number of identifiable load features in smart meter data. Our results demonstrate that our system, called PowerPlay, i) enables efficient online tracking on low-power embedded platforms, ii) scales to thousands of loads (across many buildings) on server platforms, and iii) improves per-load accuracy by more than a factor of two compared to a state-of-the-art load disaggregation algorithm.
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