Non-Intrusive Load Monitoring (NILM) is the process of deducing/identifying appliances and their energy consumption in a household. With NILM, we enable energy disaggregation – Process of decomposing power consumptions levels at appliance level from the aggregate consumption at house level.
Analyzing the load profiles and generating analytical behavior of consumption patterns can straight away help manage the grid load. In addition to that, we can also evaluate energy efficiency, recommend better practices for energy usage personalizable for each household, diagnostics, and preventive maintenances, replace expensive submetering, and many more exciting applications.
We have built an Open-Source library datastream that aids researchers and engineers to try our Deep Learning models for detection of EVs (Electric Vehicles) charging events from smart home meter data.
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