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Predictive load management in smart grid environments

 
: Mutschler, Christopher; Löffler, Christoffer; Witt, Nicolas; Edelhäußer, Thorsten; Philippsen, Michael

:

Association for Computing Machinery -ACM-, Special Interest Group on Management of Data -SIGMOD-; Association for Computing Machinery -ACM-, Special Interest Group on Software Engineering -SIGSOFT-:
8th ACM International Conference on Distributed Event-Based Systems, DEBS 2014. Proceedings : Mumbai, India, May 26-29, 2014
New York: ACM, 2014
ISBN: 978-1-4503-2737-4
pp.282-287
International Conference on Distributed Event-Based Systems (DEBS) <8, 2014, Mumbai>
English
Conference Paper
Fraunhofer IIS ()
ereignisgesteuerte Programmierung

Abstract
The DEBS 2014 Grand Challenge targets the monitoring and prediction of energy loads of smart plugs installed in private households. This paper presents details of our middleware solution and efficient median calculation, shows how we address data quality issues, and provides insights into our enhanced prediction based on hidden Markov models. The evaluation on the smart grid data set shows that we process up to 244k input events per second with an average detection latency of only 13.3ms, and that our system efficiently scales across nodes to increase throughput. Our prediction model significantly outperforms the median-based prediction as it deviates much less from the real load values, and as it consumes considerably less memory.

: http://publica.fraunhofer.de/documents/N-301475.html