Sparse Optimization for Automated Energy End Use Disaggregation


Retrieving the household electricity consumption at individual appliance level is a necessary demand to assess the contribution of different end uses to the entire household consumption, and thus to style energy saving policies and user-tailored feedback for reducing household electricity usage. This has led to the development of nonintrusive appliance load monitoring (NIALM), or energy disaggregation, algorithms, that aim to decompose the mixture energy consumption information collected from a single measurement point into device-level consumption estimations. Existing NIALM algorithms are ready to provide accurate estimate of the fraction of energy consumed by each appliance. However, in the authors’ experience, they provide poor performance in reconstructing the ability consumption trajectories overtime. In this transient, a new NIALM algorithm is presented, that, besides providing very accurate estimates of the aggregated consumption by appliance, also accurately characterizes the appliance power consumption profiles overtime. The proposed algorithm is predicated on the belief that the unknown appliance power consumption profiles are piecewise constant overtime (as it is typical for power use patterns of household appliances) and it exploits the knowledge on the time-of-day likelihood in that a specific appliance would possibly be used. The disaggregation drawback is formulated as a least-square error minimization downside, with an extra (convex) penalty term aiming at enforcing the disaggregate signals to be piecewise constant overtime. Testing on household electricity information available within the literature is reported.

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