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Prediction Models for Short-Term Load and Production Forecasting in Smart Electrical Grids

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Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10444))

Abstract

The scheduling of household smart load devices play a key role in microgrid ecosystems, and particularly in underpowered grids. The management and sustainability of these microgrids could benefit from the application of short-term prediction for the energy production and demand, which have been successfully applied and matured in larger scale systems, namely national power grids. However, the dynamic change of energy demand, due to the necessary adjustments aiming to render the microgrid self-sustainability, makes the forecasting process harder. This paper analyses some prediction techniques to be embedded in intelligent and distributed agents responsible to manage electrical microgrids, and especially increase their self-sustainability. These prediction techniques are implemented in R language and compared according to different prediction and historical data horizons. The experimental results shows that none is the optimal solution for all criteria, but allow to identify the best prediction techniques for each scenario and time scope.

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Author information

Authors and Affiliations

  1. Polytechnic Institute of Bragança, Campus Sta Apolónia, 5300-253, Bragança, Portugal

    Adriano Ferreira & Paulo Leitão

  2. Artificial Intelligence and Computer Science Laboratory, 4169-007, Porto, Portugal

    Paulo Leitão

  3. Faculty of Sciences and Technology, New University of Lisbon, Quinta da Torre, 2825-114, Caparica, Portugal

    Adriano Ferreira & José Barata

Authors
  1. Adriano Ferreira
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  2. Paulo Leitão
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  3. José Barata
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Corresponding author

Correspondence to Adriano Ferreira .

Editor information

Editors and Affiliations

  1. Czech Technical University, Prague, Czech Republic

    Vladimír Mařík

  2. German Research Center for AI, Saarbrücken, Germany

    Wolfgang Wahlster

  3. Austrian Institute of Technology, Vienna, Austria

    Thomas Strasser

  4. Czech Technical University, Prague, Czech Republic

    Petr Kadera

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Ferreira, A., Leitão, P., Barata, J. (2017). Prediction Models for Short-Term Load and Production Forecasting in Smart Electrical Grids. In: Mařík, V., Wahlster, W., Strasser, T., Kadera, P. (eds) Industrial Applications of Holonic and Multi-Agent Systems. HoloMAS 2017. Lecture Notes in Computer Science(), vol 10444. Springer, Cham. https://doi.org/10.1007/978-3-319-64635-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-64635-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64634-3

  • Online ISBN: 978-3-319-64635-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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