Skip to main content
SpringerLink
Log in

Some Issues About Iberian Energy Prices

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11621))

Included in the following conference series:

  • 1291 Accesses

Abstract

The work described in this article results from a problem proposed by the company EDP - Energy Solutions Operator, in the framework of ESGI 119th, European Study Group with Industry, during July 2016. Markets for electricity have two characteristics: the energy is mainly not storable and volatile prices at exchanges are issues to take into consideration. These two features, between others, contribute significantly to the risk of a planning process. The aim of the problem is the short term forecast of hourly energy prices. In present work, ARIMA modeling is considered to obtain a predictive model. The results show that in the time series traditional framework the season of the year, month or winter/summer period revealed significant explanatory variables in the different estimated models. The in-sample forecast is promising, conducting to adequate measures of performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.mibel.com/.

  2. 2.

    A time series is classified as stationary when it is developed in time around a constant mean.

References

  1. Eydeland, A., Wolyniec, K.: Energy and Power Risk Management: New Developments in Modeling, Pricing, and Hedging. Wiley, Hoboken (2003)

    Google Scholar 

  2. Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)

    Article  Google Scholar 

  3. Mulquiney, P.: Combining GLM and data-mining techniques for modelling accident compensation data (private communication)

    Google Scholar 

  4. Pal, S.H., Palet, J.N.: Time series data mining: a review. Bin. J. Data Min. Netw. 5, 01–04 (2015)

    Google Scholar 

  5. Ratanamahatana, C.A., Lin, J., Gunopulos, D., Keogh, E., Vlachos, M., Das, G.: Mining time series data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 1049–1077. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-09823-4_56

    Chapter  Google Scholar 

  6. Costa e Silva, E., Borges, A., Teodoro, M.F., Andrade, M.A.P., Covas, R.: Time series data mining for energy prices forecasting: an application to real data. In: Madureira, A.M., Abraham, A., Gamboa, D., Novais, P. (eds.) ISDA 2016. AISC, vol. 557, pp. 649–658. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53480-0_64

    Chapter  Google Scholar 

  7. Teodoro, M.F., Andrade, M.A.P., Silva, E.C., Borges, A., Covas, R.: Energy prices forecasting using GLM. In: Oliveira, T.A., Kitsos, C.P., Oliveira, A., Grilo, L. (eds.) Recent Studies on Risk Analysis and Statistical Modeling. CS, pp. 321–334. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76605-8_23

    Chapter  Google Scholar 

  8. Vasimalla, K.: A survey on time series data mining. Int. J. Innov. Res. Comput. Commun. Eng. 2(5), 170–179 (2014)

    Google Scholar 

  9. Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30(4), 1030–1081 (2014)

    Article  Google Scholar 

  10. Yang, Q., Wu, X.: 10 challenging problems in data mining research. Int. J. Inf. Technol. Decis. Mak. 5(04), 597–604 (2006)

    Article  Google Scholar 

  11. Zhu, Y.: High performance data mining in time series: techniques and case studies. Ph.D. thesis, Department of Computer Science, University of New York (2004)

    Google Scholar 

Download references

Acknowledgements

This work was supported by Portuguese funds through the Center of Naval Research (CINAV), Portuguese Naval Academy, Portugal and The Portuguese Foundation for Science and Technology (FCT), through the Center for Computational and Stochastic Mathematics (CEMAT), University of Lisbon, Portugal, project UID/Multi/04621/2019.

Author information

Authors and Affiliations

  1. CINAV, Center of Naval Research, Naval Academy, Portuguese Navy, 2810-001, Almada, Portugal

    M. Filomena Teodoro

  2. CEMAT, Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, Lisbon University, 1048-001, Lisboa, Portugal

    M. Filomena Teodoro

  3. ISTAR, ISCTE, Instituto Universitário de Lisboa, 1649-026, Lisboa, Portugal

    Marina A. P. Andrade

Authors
  1. M. Filomena Teodoro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marina A. P. Andrade
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Filomena Teodoro .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Teodoro, M.F., Andrade, M.A.P. (2019). Some Issues About Iberian Energy Prices. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24302-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24301-2

  • Online ISBN: 978-3-030-24302-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation