posted on 2012-08-09, 14:02authored byAdam Taylor, Edgar Galván-López, Siobhán Clarke, Vinny Cahill
A key change in the move to Smart Grids (SGs) is the use
of dynamic pricing; this together with less reliable energy
from renewable resources makes optimising electricity use
highly complex. For smart-devices to function in this envi-
ronment, they must adapt to this complexity, while main-
taining the flexibility to handle changing user behaviour pat-
terns. Reinforcement Learning (RL) has been used to op-
timise the scheduling of dynamic resources in SGs. It is
proposed to provide smart-devices with knowledge of user
intentions and actions by leveraging participatory sensing
data. This, in consequence, will allow devices in the SG to
tailor their operational schedule to users’ behaviour. With-
out this data, the devices’ operation would be interrupted
by user activity, leading to suboptimal results. Participa-
tory sensing provides for both, the monitoring of parame-
ters affecting devices operation (for example, temperature
for a heating system) and access to detailed information
about user behaviour and activity. The results obtained by
our RL approach, clearly indicate that participatory sensing
data indeed improve the performance of device scheduling
when compared to static schemes resulting in a dramatic
price reduction.
History
Publication
3rd International Workshop on Agent Technologies for Energy Systems (ATES), at AAMAS 2012;