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International Conference on Artificial Neural Networks

ICANN 2007: Artificial Neural Networks – ICANN 2007 pp 728–737Cite as

Forecasting Portugal Global Load with Artificial Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4669))

Abstract

This paper describes a research where the main goal was to predict the future values of a time series of the hourly demand of Portugal global electricity consumption in the following day. In a preliminary phase several regression techniques were experimented: K Nearest Neighbors, Multiple Linear Regression, Projection Pursuit Regression, Regression Trees, Multivariate Adaptive Regression Splines and Artificial Neural Networks (ANN). Having the best results been achieved with ANN, this technique was selected as the primary tool for the load forecasting process. The prediction for holidays and days following holidays is analyzed and dealt with. Temperature significance on consumption level is also studied. Results attained support the adopted approach.

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Authors and Affiliations

  1. Power Systems Unit of INESC Porto and Faculty of Engineering of Porto University,  

    J. Nuno Fidalgo & Manuel A. Matos

Authors
  1. J. Nuno Fidalgo
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  2. Manuel A. Matos
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Editor information

Joaquim Marques de Sá Luís A. Alexandre Włodzisław Duch Danilo Mandic

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© 2007 Springer-Verlag Berlin Heidelberg

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Fidalgo, J.N., Matos, M.A. (2007). Forecasting Portugal Global Load with Artificial Neural Networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_75

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  • DOI: https://doi.org/10.1007/978-3-540-74695-9_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74693-5

  • Online ISBN: 978-3-540-74695-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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