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Utilities have deployed tens of millions of smart meters, which record and transmit home energy usage at fine- grained intervals. These deployments are motivating researchers to develop new energy analytics that mine smart meter data to learn insights into home energy usage and behavior.
Unfortunately, a significant barrier to evaluating energy analytics is the overhead of instrumenting homes to collect aggregate energy usage data and data from each device. As a result, researchers typically evaluate their analytics on only a small number of homes, and cannot rigorously vary a home’s characteristics to determine what attributes of its energy usage affect accuracy.
To address the problem, we develop SmartSim, a publicly-available device-accurate smart home energy trace generator. SmartSim generates energy usage traces for devices by combining a device energy model, which captures its pattern of energy usage when active, with a device usage model, which specifies its frequency, duration, and time of activity. SmartSim then generates aggregate energy data for a simulated home by combining the data from each device. We integrate SmartSim with NILM-TK, a publicly- available toolkit for Non-Intrusive Load Monitoring (NILM), and compare its synthetically generated traces with traces from a real home to show they yield similar quantitative and qualitative results for representative energy analytics.
The project is in its early stages. Please note that Smart_Home_Sim is currently a research tool.
Install of SmartSim:
Please run the setup.py included in the sources.
Install of NILMTK:
Please refer to NILMTK installation instructions page to install the NILMTK toolkit.
After installation, please use "nosetests" command to test the correctness of NILMTK setup.
SmartSim: A Device-Accurate Smart Home Simulator for Energy Analytics., In Proceedings of the 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, Australia, 2016.
Dong Chen, David Irwin, Prashant Shenoy.