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 WurmEmail author
  • Vlad C. Coroamă
Special Issue Paper


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.


STLF NILM Clustering Smart grid 


  1. 1.
    Din GMU, Marnerides AK (2017) Short term power load forecasting using Deep Neural Networks. In: 2017 International conference on computing, networking and communications (ICNC). IEEE, pp 594–598Google Scholar
  2. 2.
    Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891. doi: 10.1109/5.192069 CrossRefGoogle Scholar
  3. 3.
    Kelly J, Knottenbelt W (2015) Neural nilm: deep neural networks applied to energy disaggregation. In: Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments. ACM, pp 55–64Google Scholar
  4. 4.
    Srivastava AK, Pandey AS, Singh D (2016) Short-term load forecasting methods: a review. In: International conference on emerging trends in electrical electronics & sustainable energy systems (ICETEESES). IEEE, pp 130–138Google Scholar
  5. 5.
    Wijaya TK, Humeau S, Vasirani M, Aberer K (2014) Residential electricity load forecasting: evaluation of individual and aggregate forecasts. Technical report, CiteseerGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

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

Personalised recommendations