Skip to main content
SpringerLink
Log in
Book cover

Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015 pp 181–190Cite as

Blind Source Separation for Improved Load Forecasting on Individual Household Level

  • Conference paper
  • First Online:

  • 993 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 403))

Abstract

This paper presents the improved method for 24 h ahead load forecasting applied to individual household data from a smart metering system. In this approach we decompose a set of individual forecasts into basis latent components with destructive or constructive impact on the prediction. The main research problem in such model aggregation is the proper identification of destructive components that can be treated as some noise factors. To assess the randomness of signals and thus their similarity to the noise, we used a new variability measure that helps to compare decomposed signals with some typical noise models. The experiments performed on individual household electricity consumption data with blind separation algorithms contributed to forecasts improvements.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Anda, M., Temmen, J.: Smart metering for residential energy efficiency: the use of community based social marketing for behavioural change and smart grid introduction. Renew. Energy 67, 119–127 (2014)

    Article  Google Scholar 

  2. Carroll, J., Lyons, S., Denny, E.: Reducing household electricity demand through smart metering: the role of improved information about energy saving. Energy Econ. 45, 234–243 (2014)

    Article  Google Scholar 

  3. Box, G.E.P., Jenkins, G.M.: Time Series Analysis. Forecasting and Control. Prentice-Hall, Englewood Cliffs (1994)

    MATH  Google Scholar 

  4. Hamilton, J.D.: Time Series Analysis. Princeton University Press, Princeton (1994)

    MATH  Google Scholar 

  5. Szupiluk, R., Za̧bkowski, T.: In: Rutkowski, et al. (eds.) Signal Randomness Measure for BSS Ensemble Predictors. Lecture Notes in Computer Science, vol. 8468, pp. 570–578. Springer, Berlin (2014)

    Google Scholar 

  6. Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing. Wiley, Chichester (2002)

    Book  Google Scholar 

  7. Comon, P., Jutten, C.: Handbook of Blind Source Separation: Independent Component Analysis and Applications. Academic Press, Boston (2010)

    Google Scholar 

  8. Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, New York (2001)

    Book  Google Scholar 

  9. Szupiluk, R., Cichocki, A.: Blind signal separation using second order statistics. Proc. SPETO 2001, 485–488 (2001)

    Google Scholar 

  10. Dataport. https://dataport.pecanstreet.org/ (2014)

  11. Gajowniczek, K., Za̧bkowski, T.: Short term electricity forecasting using individual smart meter data. Procedia Comput. Sci. 35, 589–597 (2014)

    Article  Google Scholar 

  12. Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)

    Article  MathSciNet  Google Scholar 

  13. National Research Council: Estimating Population and Income for Small Places. National Academy Press, Washington (1980)

    Google Scholar 

  14. Moreno, J.J.M., Pol, A.P., Abad, A.S., Blasco, B.C.: Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema 25(4), 500–506 (2013)

    Google Scholar 

  15. Huber, P.J.: Robust Statistics. Wiley, New York (1981)

    Book  MATH  Google Scholar 

  16. R Development Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org/ (2011)

  17. Kuhn, M.: Building predictive models in r using the caret package. J. Stat. Softw. 28(5), 1–26 (2008)

    Article  Google Scholar 

  18. Dai, Y.D.: Convergence properties of the BFGS algorithm. SIAM J. Opt. 13(3), 693–701 (2002)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

The study is cofounded by the European Union from resources of the European Social Fund. Project PO KL âInformation technologies: Research and their interdisciplinary applicationsâ, Agreement UDA-POKL.04.01.01-00-051/10-00.

Author information

Authors and Affiliations

  1. Department of Informatics, Warsaw University of Life Sciences, Warsaw, Poland

    Krzysztof Gajowniczek

  2. Warsaw School of Economics, Warsaw, Poland

    Tomasz Za̧bkowski

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Ryszard Szupiluk

Authors
  1. Krzysztof Gajowniczek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomasz Za̧bkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryszard Szupiluk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krzysztof Gajowniczek .

Editor information

Editors and Affiliations

  1. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Robert Burduk

  2. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Konrad Jackowski

  3. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Marek Kurzyński

  4. Dept. of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michał Woźniak

  5. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Andrzej Żołnierek

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Gajowniczek, K., Za̧bkowski, T., Szupiluk, R. (2016). Blind Source Separation for Improved Load Forecasting on Individual Household Level. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26227-7_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26225-3

  • Online ISBN: 978-3-319-26227-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation