IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v17y2020i1d10.1007_s10287-018-0320-2.html
   My bibliography  Save this article

Modelling an energy market with Bayesian networks for non-normal data

Author

Listed:
  • Vincenzina Vitale

    (Università Roma Tre)

  • Flaminia Musella

    (Link Campus University)

  • Paola Vicard

    (Università Roma Tre)

  • Valentina Guizzi

    (Università Roma Tre)

Abstract

Energy markets are typically characterized by high complexity due to several reasons such as the large number of occurring variables, different in nature, and their associative structure. Estimating a statistical model that properly represents the dependencies among the variables is crucial for managing such a complexity. In this paper, a simple energy market influenced by hydroelectric availability is studied by using Bayesian networks. Since the variables of interest are quantitative but non Gaussian, non-parametric strategies are used to infer the Colombian energy market association structure. We propose a comparison between the UniNet learning algorithm and the Rank PC algorithm, both based on normal copula assumption and Spearman correlation measure, in order to explore differences in the estimated models. Finally, model usability for energy managers is shown through the discussion of some scenarios.

Suggested Citation

  • Vincenzina Vitale & Flaminia Musella & Paola Vicard & Valentina Guizzi, 2020. "Modelling an energy market with Bayesian networks for non-normal data," Computational Management Science, Springer, vol. 17(1), pages 47-64, January.
    • Handle: RePEc:spr:comgts:v:17:y:2020:i:1:d:10.1007_s10287-018-0320-2
    DOI: 10.1007/s10287-018-0320-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10287-018-0320-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10287-018-0320-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hanea, A.M. & Kurowicka, D. & Cooke, R.M. & Ababei, D.A., 2010. "Mining and visualising ordinal data with non-parametric continuous BBNs," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 668-687, March.
    2. Kalisch, Markus & Mächler, Martin & Colombo, Diego & Maathuis, Marloes H. & Bühlmann, Peter, 2012. "Causal Inference Using Graphical Models with the R Package pcalg," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i11).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hobæk Haff, Ingrid & Aas, Kjersti & Frigessi, Arnoldo & Lacal, Virginia, 2016. "Structure learning in Bayesian Networks using regular vines," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 186-208.
    2. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    3. Pierpaolo D’Urso & Vincenzina Vitale, 2020. "Bayesian Networks Model Averaging for Bes Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(3), pages 897-919, October.
    4. Bettendorf, Timo & Heinlein, Reinhold, 2019. "Connectedness between G10 currencies: Searching for the causal structure," Discussion Papers 06/2019, Deutsche Bundesbank.
    5. Bouncken, Ricarda B. & Ratzmann, Martin & Kraus, Sascha, 2021. "Anti-aging: How innovation is shaped by firm age and mutual knowledge creation in an alliance," Journal of Business Research, Elsevier, vol. 137(C), pages 422-429.
    6. Pierpaolo D’Urso & Vincenzina Vitale, 2021. "Modeling Local BES Indicators by Copula-Based Bayesian Networks," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(3), pages 823-847, February.
    7. Leonard Henckel & Emilija Perković & Marloes H. Maathuis, 2022. "Graphical criteria for efficient total effect estimation via adjustment in causal linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 579-599, April.
    8. Peter Bühlmann, 2013. "Causal statistical inference in high dimensions," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 77(3), pages 357-370, June.
    9. Aviral Kumar Tiwari & Micheal Kofi Boachie & Rangan Gupta, 2021. "Network Analysis of Economic and Financial Uncertainties in Advanced Economies: Evidence from Graph-Theory," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(1), pages 188-215, March.
    10. Jenny Häggström, 2018. "Rejoinder to Discussions on: Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 407-410, June.
    11. Rigana, Katerina & Wit, Ernst-Jan Camiel & Cook, Samantha, 2023. "A new way of measuring effects of financial crisis on contagion in currency markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    12. Jinyang Zheng & Zhengling Qi & Yifan Dou & Yong Tan, 2019. "How Mega Is the Mega? Exploring the Spillover Effects of WeChat Using Graphical Model," Information Systems Research, INFORMS, vol. 30(4), pages 1343-1362, December.
    13. Pierpalo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A Bayesian network to analyse basketball players’ performances: a multivariate copula-based approach," Annals of Operations Research, Springer, vol. 325(1), pages 419-440, June.
    14. Flaminia Musella & Paola Vicard & Maria Chiara De Angelis, 2022. "A Bayesian Network Model for Supporting School Managers Decisions in the Pandemic Era," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(3), pages 1445-1465, October.
    15. Morales-Nápoles, Oswaldo & Steenbergen, Raphaël D.J.M., 2014. "Analysis of axle and vehicle load properties through Bayesian Networks based on Weigh-in-Motion data," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 153-164.
    16. Daniela Marella, 2018. "Pc Complex: Pc Algorithm For Complex Survey Data," Departmental Working Papers of Economics - University 'Roma Tre' 0240, Department of Economics - University Roma Tre.
    17. Ronja Foraita & Juliane Friemel & Kathrin Günther & Thomas Behrens & Jörn Bullerdiek & Rolf Nimzyk & Wolfgang Ahrens & Vanessa Didelez, 2020. "Causal discovery of gene regulation with incomplete data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1747-1775, October.
    18. Michimasa Fujiogi & Yoshihiko Raita & Marcos Pérez-Losada & Robert J. Freishtat & Juan C. Celedón & Jonathan M. Mansbach & Pedro A. Piedra & Zhaozhong Zhu & Carlos A. Camargo & Kohei Hasegawa, 2022. "Integrated relationship of nasopharyngeal airway host response and microbiome associates with bronchiolitis severity," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    19. C. Wittenbecher & R. Cuadrat & L. Johnston & F. Eichelmann & S. Jäger & O. Kuxhaus & M. Prada & F. Del Greco M. & A. A. Hicks & P. Hoffman & J. Krumsiek & F. B. Hu & M. B. Schulze, 2022. "Dihydroceramide- and ceramide-profiling provides insights into human cardiometabolic disease etiology," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    20. C Schultheiss & P Bühlmann, 2023. "Ancestor regression in linear structural equation models," Biometrika, Biometrika Trust, vol. 110(4), pages 1117-1124.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comgts:v:17:y:2020:i:1:d:10.1007_s10287-018-0320-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.