In an earlier post
I discussed the utility (or otherwise) of the 24 hour power consumption
graph and questioned why Google and Microsoft were both investing in
this approach to home energy efficiency. Since then both Google and
Microsoft have stopped their efforts in this area.
Interestingly, in Europe I'm seeing more and more homes with devices
like the one shown here that provide real-time power consumption
information. One of the more interesting uses for devices like these is
as a check that everything has been turned off when a homeowner is about
to leave the house. A quick glance at the meter can reveal if a heater
has been left on in a bedroom. Of course the main water heater has the
largest impact on the reading but homeowners learn what numbers
represent 'normal' and can see at a glance when something else has been
left on. Clearly a true smart home that can turn devices off when they
are no longer in use is still a better long-term solution for this
scenario but it's interesting to see how a fairly simple device can at
least provide an indication that everything is off without a significant
investment in replacing light switches and device controllers. What
would be nicer however would be if the meter included some kind of
machine learning so it could show at a glance if the home is in a
minimal power state or not.
I've been working on home automation for over 15 years and I'm close to achieving my goal which is a house that understands where everyone is at all times, can predict where you are going next and can control lighting, heating and other systems without you having to do or say anything. That's a true "smart home".
An overview of the many sensors I've experimented with for home automation including my favorite under-floor strain gauge, through all the usual PIR, beam and contact sensors to some more esoteric devices like an 8x8 thermal camera.
Home automation systems need to respond to events in the real world. Sometimes it's an analog value, sometimes it's binary, rarely is it clean and not susceptible to problems. Let's discuss some of the ways to convert these inputs into actions.
Another super useful function for handling sensor data and converting to probabilities is the logistic function 1/(1+e^-x). Using this you can easily map values onto a 0.0-1.0 probability range.
In a home automation system we often want to convert a measurement into a probability. The ATAN curve is one of my favorite curves for this as it's easy to map overything onto a 0.0-1.0 range.
An if-this-then-that style rules machine is insufficient for lighting control. This state machine accomplishes 90% of the correct behavior for a light that is controlled automatically and manually in a home automation system.
