Management and Control of Energy Usage and Price using Participatory Sensing Data

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 environment, they must adapt to this complexity, while maintaining the flexibility to handle changing user behaviour patterns. Reinforcement Learning (RL) has been used to optimise 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. Without this data, the devices’ operation would be interrupted by user activity, leading to suboptimal results. Participatory sensing provides for both, the monitoring of parameters 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.