Comput Manag Sci https://doi.org/10.1007/s10287-018-0320-2 ORIGINAL PAPER Modelling an energy market with Bayesian networks for non-normal data 1 2 Vincenzina Vitale · Flaminia Musella · 1 1 Paola Vicard · Valentina Guizzi Received: 14 January 2017 / Accepted: 23 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Energy markets are typically characterized by high complexity due to sev- eral 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 stud- ied by using Bayesian networks. Since the variables of interest are quantitative but non Gaussian, non-parametric strategies are used to infer the Colombian energy mar- ket 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. Keywords Hydroelectric market · Dependence modelling · Joint normal copula · Rank-based correlation 1
Computational Management Science – Springer Journals
Published: Jun 1, 2018
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