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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2020: ECML PKDD 2020 Workshops pp 62–74Cite as

Data Decomposition Based Learning for Load Time-Series Forecasting

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1323))

Abstract

With the deployment of smart grid technologies and management modernization, the large amount of fine-grained electricity consumption data become readily available. So, the process of knowledge extraction from such a vast amount of data should be optimized to efficiently and reliably utilize such information for future strategies making, network optimization and power system planning. In this context, we proposed a hybrid approach that combines decomposition algorithm with deep learning model for improved accuracy and reduced complexity. The approach adds an additional novel perspective to the existing studies by selecting appropriate prediction models on the basis of decomposed components intrinsic features. Experiments are conducted on the Australian National Electricity Market (specifically, Queensland) dataset and prediction results are compared to two different state-of-the-art decomposition based hybrid approaches. The comparison results are evidence that the proposed approach outperforms other decomposition based hybrid approaches by generating 1.63% average prediction error.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, 247667, India

    Jatin Bedi & Durga Toshniwal

Authors
  1. Jatin Bedi
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  2. Durga Toshniwal
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Corresponding author

Correspondence to Jatin Bedi .

Editor information

Editors and Affiliations

  1. University of Sydney, Sydney, NSW, Australia

    Irena Koprinska

  2. Monash University, Clayton, VIC, Australia

    Michael Kamp

  3. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  4. University of Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  5. University of Guelph, Guelph, ON, Canada

    Luiza Antonie

  6. University of Caen Normandy, Caen, France

    Albrecht Zimmermann

  7. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  8. Norwegian University of Science and Technology, Trondheim, Norway

    Özlem Özgöbek

  9. University of Porto, Porto, Portugal

    Rita P. Ribeiro

  10. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  11. University of Porto, Porto, Portugal

    João Gama

  12. Fraunhofer IAIS, St. Augustin, Germany

    Linara Adilova

  13. Royal Holloway University of London, Egham, UK

    Yamuna Krishnamurthy

  14. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  15. University of Bari Aldo Moro, Bari, Italy

    Donato Malerba

  16. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  17. University of Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  18. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  19. University of Naples Federico II, Naples, Italy

    Elio Masciari

  20. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Ras

  21. Australian National University, Canberra, ACT, Australia

    Peter Christen

  22. Leibniz University Hannover, Hannover, Germany

    Eirini Ntoutsi

  23. Technical University of Dortmund, Dortmund, Germany

    Erich Schubert

  24. University of Southern Denmark, Odense, Denmark

    Arthur Zimek

  25. University of Pisa, Pisa, Italy

    Anna Monreale

  26. Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Biecek

  27. ISTI-CNR, PISA, Italy

    Salvatore Rinzivillo

  28. Berlin Institute of Technology, Berlin, Germany

    Benjamin Kille

  29. Berlin Institute of Technology, Berlin, Germany

    Andreas Lommatzsch

  30. Norwegian University of Science and Technology, Trondheim, Norway

    Jon Atle Gulla

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Bedi, J., Toshniwal, D. (2020). Data Decomposition Based Learning for Load Time-Series Forecasting. In: Koprinska, I., et al. ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-65965-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-65965-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65964-6

  • Online ISBN: 978-3-030-65965-3

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