Digital Twins and Machine Learning


(Crispin) #21

I’d be interested in this too. I’ve been waiting / hoping for something like this. :smile:


(Christoph B) #22

do you guys have any specific ideas on what to use it for?


(-) #23

One area that I’ve been thinking of is whether something like this can provide easier/better control of home heating than the usual rule based solutions, taking into account when people are at home, day of week etc, but also outside weather conditions. Not quite a Nest, but something that can benefit from the increased data that we have available in an openHAB type of system.

A learning solution would also be much easier to manage when there are many zones (e.g. the evohome smart thermostat system has 12 zones - which would then possibly require 12 lots of manual rules etc).


(Kristof Rado) #24

I just realised this thread now :slight_smile: I have been thinking of the same for months now, never thought that someone tried to implement such thing. Your high rates are looking great. Do you have any suggestions what I should do in order to prepare openHab for something like this? Because I thought that if I have enough sensors/actuators and persisted data for a bigger period of time (1 year for example) it can be a good start for it.
Do you have rules defined in openHab besides this neural network? It won’t effect them? Like it learns that a proxy switch receives a command then some other switches be turned on, so it overrides them or sends the command duplicated… all your items are exposed to the neural network?

Also what hardware are you running this system on? Trainings might need a powerful computer, right? Besides training, is it using significant resources?

Thanks!


(Bob Miles) #25

I’m very interested in the mode of your implementation, too!

Probably the preprocessing of the data is the biggest problem - but maybe it could be generalized for some item categories like switches or dimmer.


(Kristof Rado) #26

I will share it as soon as I have something. However this is a long-range project for me, so now I just started grabbing as many info as I can before I start to implement my way. I have to make my OH as stable as I can and also buy a more powerful server…


(Bob Miles) #27

I plan to start off with only one light. Tomorrow I’ll try to grab as much data from the past months as I can get.
after normalizing it, hopefully I can train a simple net using the time of day and presence as parameter and the on off states as output classifiers.
not sure about the hidden layer though!

Update: I have done some experiments with the history data of my kitchen light. Just extracted the data with time stamps and trained a classifier for ON and OFF using a 3 layer NN in self written python code.
I have not yet included presence as parameter, getting about 70 percent confidence on the training and the validation set.