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This paper presents a new algorithm to extract device profiles fullyunsupervised from three phases reactive and active aggregate powermeasurements. The extracted device profiles are applied for the disaggregationof the aggregate power measurements using particle swarm optimization. Finally,this paper provides a new approach for short term power predictions using thedisaggregation data. For this purpose, a state changes forecast for everydevice is carried out by an artificial neural network and converted into apower prediction afterwards by reconstructing the power regarding the statechanges and the device profiles. The forecast horizon is 15 minutes. Todemonstrate the developed approaches, three phase reactive and active aggregatepower measurements of a multi-tenant commercial building are used. Thegranularity of data is 1 s. In this work, 52 device profiles are extracted fromthe aggregate power data. The disaggregation shows a very accuratereconstruction of the measured power with a percentage energy error ofapproximately 1 %. The developed indirect power prediction method applied tothe measured power data outperforms two persistence forecasts and an artificialneural network, which is designed for 24h-day-ahead power predictions workingin the power domain.

A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings