Learning energy consumption profiles from data

A first step in the optimisation of the power consumption of a device infrastructure is to detect the power consumption signature of the involved devices. In this paper, we are especially interested in devices which spend most of their time waiting for a job to execute, as is often the case of shared devices in a networked infrastructure, like multi-function printing devices in an office or transaction processing terminals in a public service. We formulate the problem as an instance of power disaggregation in non intrusive load monitoring (NILM), with strong prior assumptions on the sources but with specific constraints: in particular, the aggregation is occlusive rather than additive.We use a specific variant of Hidden Semi Markov Models (HSMM) to build a generative model of the data, and adapt the Expectation-Maximisation (EM) algorithm to that model, in order to learn, from daily operation data, the physical characteristics of the device, separated from those linked to the job load or the device configurations. Finally, we show some experimental results on a multifunction printing device.