Name
Abstract | ||
|---|---|---|
Many energy optimizations require fine-grained, load-level energy data collected in real time, most typically by a plug-level energy meter. Online load tracking is the problem of monitoring an individual electrical load’s energy usage in software by analyzing the building’s aggregate smart meter data. Load tracking differs from the well-studied problem of load disaggregation in that it emphasizes per-load accuracy and efficient, online operation 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 networked-connected power meter attached to the load, but notably does not require tracking every other load as well. We propose PowerPlay, a model-driven system for performing accurate, high-performance online load tracking. Our results from applying the system to real-world energy data demonstrate that 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. Our results point to the potential of replacing physical energy meters by “virtual” power meters using a system like PowerPlay.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3303860 | TCPS |
Keywords | Field | DocType |
Online load tracking, load modeling, smart grid, smart meters | Smart grid,Metre,Electrical load,Load modeling,Real-time computing,Software,Offline analysis,Smart meter,Engineering,Electricity meter | Journal |
Volume | Issue | ISSN |
3 | 2 | 2378-962X |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sean Kenneth Barker | 1 | 128 | 7.47 |
| Sandeep Kalra | 2 | 6 | 1.14 |
| David E. Irwin | 3 | 899 | 98.12 |
| Prashant J. Shenoy | 4 | 6386 | 521.30 |