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

Modelling an energy market with Bayesian networks for non-normal data 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Management Science Springer Journals

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

Loading next page...
 
/lp/springer_journal/modelling-an-energy-market-with-bayesian-networks-for-non-normal-data-Jp3mBgutDR
Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Business and Management; Operations Research/Decision Theory; Optimization
ISSN
1619-697X
eISSN
1619-6988
D.O.I.
10.1007/s10287-018-0320-2
Publisher site
See Article on Publisher Site

Abstract

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

Journal

Computational Management ScienceSpringer Journals

Published: Jun 1, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off