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arXiv · 1708.04613

Real-time Load Prediction with High Velocity Smart Home Data Stream

Christoph Doblander·Martin Strohbach·Holger Ziekow·Hans-Arno Jacobsen
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Abstract

This paper addresses the use of smart-home sensor streams for continuous prediction of energy loads of individual households which participate as an agent in local markets. We introduces a new device level energy consumption dataset recorded over three years wich includes high resolution energy measurements from electrical devices collected within a pilot program. Using data from that pilot, we analyze the applicability of various machine learning mechanisms for continuous load prediction. Specifically, we address short-term load prediction that is required for load balancing in electrical micro-grids. We report on the prediction performance and the computational requirements of a broad range of prediction mechanisms. Furthermore we present an architecture and experimental evaluation when this prediction is applied in the stream.

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Christoph Doblander, Martin Strohbach, Holger Ziekow, Hans-Arno Jacobsen. 2017-08-12. Real-time Load Prediction with High Velocity Smart Home Data Stream. https://arxiv.org/abs/1708.04613

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