Poster Abstract: Real-time load prediction with high velocity smart home data stream


This poster addresses the use of smart home data to continuously predict the aggregated energy consumption of individual households. We introduce a device level energy consumption dataset recorded over 3 years wich includes high resolution energy measurements from electrical devices collected within a pilot program. Using data from that pilot, we analyze the performance of various machine learning mechanisms for continuous short-term load prediction.

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This work has been supported in part by the Alexander von Humboldt foundation, the H2020 project HOBBIT under the Grant Agreement Number 688227 and by the German Federal Ministry of Economics and Technology, project PeerEnergyCloud which is part of the Trusted Cloud Program.

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Correspondence to Christoph Doblander.

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Doblander, C., Strohbach, M., Ziekow, H. et al. Poster Abstract: Real-time load prediction with high velocity smart home data stream. Comput Sci Res Dev 33, 233–234 (2018).

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  • Electrical load prediction
  • Machine learning