  This paper provides a formalization of the energy disaggregation problem for
particle swarm optimization and shows the successful application of particle
swarm optimization for disaggregation in a multi-tenant commercial building.
The developed mathmatical description of the disaggregation problem using a
state changes matrix belongs to the group of non-event based methods for energy
disaggregation. This work includes the development of an objective function in
the power domain and the description of position and velocity of each particle
in a high dimensional state space. For the particle swarm optimization, four
adaptions have been applied to improve the results of disaggregation, increase
the robustness of the optimizer regarding local optima and reduce the
computational time. The adaptions are varying movement constants, shaking of
particles, framing and an early stopping criterion. In this work we use two
unlabelled power datasets with a granularity of 1 s. Therefore, the results are
validated in the power domain in which good results regarding multiple error
measures like root mean squared error or the percentage energy error can be
shown.
