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Neural Network & Machine Learning: Nonintrusive Load Monitoring & Energy Disaggregation

emonpi
Tags: #<Tag:0x00007f10a5191880>
(Will Buchanan) #1

I wanted to share a proof of concept energy disaggregation appliance classifier we built using the EmonPi. It uses a simple neural network to classify what appliance is in use. While it’s got a ton of potential, due to time constraints we didn’t develop overlapping/concurrent appliance operation. It is equipped for a power strip installation for proof of concept. We documented everything so hopefully someone will take it further!

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(Trystan Lea) #2

Thankyou for sharing @buchananwp this looks fantastic! I will take some time to read through you github repository.

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(Will Buchanan) #3

Thanks @TrystanLea for developing and open-sourcing the emonPi!

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(Simon) #4

Did anyone notice this from ST Micro

STM32 Solutions for Artificial Neural Networks

Not sure Trystan what progress has been made on the STM32 design but this could be interesting.

Simon

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(dBC) #5

Per-breaker monitoring, with load recognition per channel might be a powerful combo. There’d be a few portable loads like vacuum cleaners that might get dragged from breaker to breaker, but most loads never change circuits.

My per-breaker monitor tells me that 42% of my long term power usage (90% at the time of capture) happens through one of the General Purpose Outlets (aka power points) breakers, which isn’t particularly useful for determining where the big consumers are:

gpo1
(excuse the missing % symbols… I think some font change meant that no longer shows up and I’ve not gone in to fix it yet).

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