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Different model types for short-term forecasting of characteristic load points

  • Part VII:Prediction, Forecasting, and Monitoring
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

This paper presents Neural Network solutions for 24 hours ahead electrical load forecasting of normal working days in Belgium. Our approach introduces several models, each one predicting a different characteristic load point of the day. The differences in behaviour between these load points led us to such differentiation as opposed to homogeneous load curve forecasting. Results compared to the human experts' forecasts indicate the neural networks perform adequately.

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References

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Authors and Affiliations

  1. Energy Engineering Division - TRACTEBEL S.A. Avenue Ariane 7, B-1200, Brussels, Belgium

    Myriam Monteyne, Tristan Salomè, Françoise de Viron, Jean-Michel Renders, Pierre Doulliez, François Dongier & Jean Claus

Authors
  1. Myriam Monteyne
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  2. Tristan Salomè
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  3. Françoise de Viron
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  4. Jean-Michel Renders
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  5. Pierre Doulliez
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  6. François Dongier
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  7. Jean Claus
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Monteyne, M. et al. (1997). Different model types for short-term forecasting of characteristic load points. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020285

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  • DOI: https://doi.org/10.1007/BFb0020285

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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