Architecture of Computing Systems (Lecture Notes in Computer ...

158 B. Becker et al.
freedom like washing machines or thermal storages can be re-scheduled several
times in compliance with its local constraints. A stove or a multimedia device,
on the other hand, has an extremely poor degree of freedom, so their demand
profile can be observed, but there is no possibility of re-scheduling the operation.
In our scenario, the degree of freedom is determined by each appliance or
by the smart-home’s resident using the web-interface. Some appliances have to
execute a specific program before they can be switched off. Thus, the specific
profile for these appliances must be respected in the SHMD while generating the
re-scheduling of the appliances. Therefore, the local controller of each appliance
has a set of static rules. The decision to change the operation state depends on
these rules combined with the rule set from the SHMD.
5.2 Central Observer
The smart-home’s observer unit captures power changes for each intelligent appliance,
the charge condition of the electric vehicle’s battery, the devices’ degree
of freedom, and the known device profiles. The latter is received from the intelligent
appliance itself or calculated from the past power data. The whole set of
values is stored in the SHMD’s database. Reduced to the essential, the database
contains a list of the attributed set of intelligent appliances and the data history
of the power demand for each of them.
Using a sufficient quantity of history data, it is possible to build a prediction,
described in Fig. 4, for each day of the week. The history items are classified in
data sets by the day of week and a certain timeslot. An appropriate data structure
is a tree containing for each intelligent appliance a node on the first level.
Each of these nodes branches into seven nodes on the second level, representing
the seven days of the week. The weekday node has subnodes for a fixed set of
timeslots (e. g., blocks à 5 minutes) of the day. Finally, every data set from the
intelligent appliances’s history is associated to one of the timeslot nodes.
This part’s challenge is to get timeslots, where an appliance is frequently
switched on. Each timeslot is represented by the corresponding daytime and
weekday. To find these frequent item sets in the tree structure, the timeslot
nodes can be easily scanned for such nodes having more than a certain number of
leaves in the tree. Having found these timeslots, it is obvious that the associated
device will probably be powered up on the same weekday’s timeslot during the
next weeks. To increase the rate of adaption a digressive weighting depending
on the items’ age is used. Furthermore, this knowledge can be transformed into
a power forecast for each intelligent appliance for the next days.
5.3 Central Controller
Reverting to an extensive pool of data collected by the observer of the SHMD,
the main controller’s basic task is to send adequate rule sets to the subordinated
local controllers of the intelligent appliances. The final decision is made by the
intelligent appliance’s controller, but it should be mostly consistent with the rule
sets of the main controller. To simplify our initial approach to the smart-home