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Computer Science - Research and Development

, Volume 33, Issue 1–2, pp 265–266 | Cite as

Poster abstract: grid-level short-term load forecasting based on disaggregated smart meter data

  • Maximilian Wurm
  • Vlad C. Coroamă
Special Issue Paper
  • 165 Downloads

Abstract

The rollout of smart meters and steadily increasing sample rates lead to a growing amount of raw data available for short-term load forecasting (STLF). While the original motivation for high resolutions has been the enabling of non-intrusive load monitoring (NILM), so far their value for STLF has been limited. We propose a novel approach, which allows the exploitation of high resolution data for STLF, by incorporating NILM and subsequent clustering of similarly behaving appliances as a preprocessing step.

Keywords

STLF NILM Clustering Smart grid 

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Distributed Systems Group, Institute for Pervasive ComputingETH ZurichZurichSwitzerland

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