  This paper presents a new algorithm to extract device profiles fully
unsupervised from three phases reactive and active aggregate power
measurements. The extracted device profiles are applied for the disaggregation
of the aggregate power measurements using particle swarm optimization. Finally,
this paper provides a new approach for short term power predictions using the
disaggregation data. For this purpose, a state changes forecast for every
device is carried out by an artificial neural network and converted into a
power prediction afterwards by reconstructing the power regarding the state
changes and the device profiles. The forecast horizon is 15 minutes. To
demonstrate the developed approaches, three phase reactive and active aggregate
power measurements of a multi-tenant commercial building are used. The
granularity of data is 1 s. In this work, 52 device profiles are extracted from
the aggregate power data. The disaggregation shows a very accurate
reconstruction of the measured power with a percentage energy error of
approximately 1 %. The developed indirect power prediction method applied to
the measured power data outperforms two persistence forecasts and an artificial
neural network, which is designed for 24h-day-ahead power predictions working
in the power domain.
