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 SmartSim is currently a research tool.
Please run the setup.py included in the sources.
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.
Please go Wiki pages.
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.