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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
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    References listed on IDEAS

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    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).
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