Bayesian networks based policy making in the renewable energy sector

Moldir Zholdasbayeva, Vasilios Zarikas, Stavros Poulopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Extensive research on energy policy nowadays combines theory with advanced statistical tools such as Bayesian networks for analysis and prediction. The majority of these studies are related to observe energy scenarios in various economic or social conditions, but only a few of them target the renewable energy sector. Therefore, it is crucial to design a method to understand the causal relationships between variables such as consumption, greenhouse emissions, investment in renewables and investment in fossil fuels. This research paper aims to present expert models using the capabilities of Bayesian networks in the renewable energy sector, considering renewables in two countries: Germany and Italy. For this purpose, expert models are built in BayesiaLab with supervised learning. An augmented naïve model is applied to quantitative data consisting of the consumption rate of geothermal and hydro energy sectors. As a result, it is indicated that in the optimum case, geothermal and hydro energy consumption will be increased in parallel with investment. It is found that, as oil price grows, greenhouse emissions will decrease. The precision of the expert model is no less than 90%.

Original languageEnglish
Title of host publicationICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
EditorsAna Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages453-462
Number of pages10
ISBN (Electronic)9789897583957
Publication statusPublished - Jan 1 2020
Event12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta
Duration: Feb 22 2020Feb 24 2020

Publication series

NameICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference12th International Conference on Agents and Artificial Intelligence, ICAART 2020
CountryMalta
CityValletta
Period2/22/202/24/20

    Fingerprint

Keywords

  • Bayesian Networks
  • Expert Models
  • Geothermal Energy
  • Hydro Energy
  • Renewable Energy

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Zholdasbayeva, M., Zarikas, V., & Poulopoulos, S. (2020). Bayesian networks based policy making in the renewable energy sector. In A. Rocha, L. Steels, & J. van den Herik (Eds.), ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence (pp. 453-462). (ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence; Vol. 2). SciTePress.