Prediction models for short-term load and production forecasting in smart electrical grids

Adriano Ferreira, Paulo Leitão, José Barata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationIndustrial Applications of Holonic and Multi-Agent Systems - 8th International Conference, HoloMAS 2017, Proceedings
PublisherSpringer Verlag
Pages186-199
Number of pages14
Volume10444 LNAI
ISBN (Print)9783319646343
DOIs
Publication statusPublished - 2017
Event8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2017 - Lyon, France
Duration: 28 Aug 201730 Aug 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10444 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2017
CountryFrance
CityLyon
Period28/08/1730/08/17

Keywords

  • Microgrids sustainability
  • Multi-agent systems
  • Prediction models

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  • Cite this

    Ferreira, A., Leitão, P., & Barata, J. (2017). Prediction models for short-term load and production forecasting in smart electrical grids. In Industrial Applications of Holonic and Multi-Agent Systems - 8th International Conference, HoloMAS 2017, Proceedings (Vol. 10444 LNAI, pp. 186-199). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10444 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-64635-0_14