There are no standards here yet, and no right or wrong answer for every
situation. In our first example, the primary use case for the system is to support
remote access. If the user is out and about and turns the heating on remotely
using the phone app, the 2 minute delay is not noticeable. The house will be
warm when the user gets home. Even if the user is in, heating is the type of
system that operates on a timescale of hours, so unlike a light switch, down to
the second responsiveness isn’t necessarily needed. If the user is standing in
front of the heating controller with the smartphone app, it may be confusing,
but the compromise may be acceptable.
In other situations, any delay or uncertainty about whether a command has
been executed might be dangerous. For example, a person who presses an
emergency alarm button must be absolutely confident their call for help has
been sent and received. In this case, the UI should not make it appear that the
system has received and acted on their command until it has definitely done
so.
The frequency with which data is synchronized around the system can heavily
shape the user value of the service. For example, dual fuel smart meters
monitoring gas and electricity usage may report data at different frequencies
for each fuel. The device monitoring electricity usage can run on mains power,
so it can report data every few seconds. However, it would be dangerous to
place a mains powered electrical device on a gas pipe. So the gas monitoring
device will be battery powered. To maintain acceptable battery life, gas data
will be reported less frequently than electricity data, perhaps only every 30
minutes.
With live electricity data, users can turn devices on and off and use a display
(see figure 9. 31 ) or smartphone to view almost immediately the energy impact
each device had. The system can be used to understand the energy
consumption of specific appliances and behaviors, such as boiling a kettle, or
turning on a clothes dryer.
Figure 9.31: an in-home display
With gas data in half hour chunks, it’s harder for the user to relate
consumption to specific gas consuming activities in the home. You can’t see
the immediate impact of turning on a gas cooker. Was the last half hour’s
consumption high because the oven was on, or because the heating or hot
water was in use? So the system data does not directly answer the question