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Non-intrusive load monitoring (NILM) and appliance signature identification [international]

posted Sep 23, 2010, 3:50 AM by ema-1 ema-1   [ updated Aug 7, 2012, 5:44 PM ]

[Updated content available here: NILM and similar methods]

In recent press Navetas states: "while home energy monitor products have recently become popular, these have limited functionality as they only provide an estimate of usage. The problem for the householder is that the final bill can vary widely from this estimate. Our solution provides an accurate measure of the actual energy usage and can be broken down by appliance. It is possible then to compare the efficiency of appliances and see, at a glance, where energy is being used".

Navetas offers an advanced home energy monitor which does not require training of the device to recognise each individual appliance. The system can be retrofitted by electrical utility providers. Navetas is a UK company and it possible their product will work in New Zealand without significant modification.

A combination of active and reactive power used when appliances are switched on can be used to identify particular appliances and measure energy use for those appliances. Each appliance can be identified and monitored using this unique signature within the household supply.

Enetics in the USA offer professional systems for integration with existing smart meters, and standalone systems. Enetics also provide data collection and analysis if needed.

Intel have been working on appliance signature detection products for some time. Intel's devices so far require limited training of the monitoring device to recognise each individual appliance profile. Intel demonstrated the system at the 2010 developer forum smart energy presentation (@19:49).

Ireland's Episensor also requires specific training.

The Energy Detective Footprints software available for the TED 1000 and TED 5000 has a load profiling feature. This feature is best able to recognise larger constant loads but has trouble recognising some variable and complex appliance loads. It is unlikely this system uses complex NILM methods.

Many users of power monitoring products already perform their own simple profiling and analysis. For instance: If the electrical load increases by 3000 watts and the only 3000 watt load in the house is the hot water heating element: It is a safe conclusion that a 3000 watt increase or decrease can be attributed to the hot water system switching on or off. Users tracking this data on a PC or monitoring an internet service like Google PowerMeter are able to intuit the on/ off times of particular appliance loads using similar methods.

Distribution of appliance use data has obvious privacy and security implications. In addition there are large potential benefits for energy efficiency and conservation through accurate knowledge of in-service energy profiles.




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