Bayesian Networks based Policy Making in the Renewable Energy Sector

Moldir Zholdasbayeva, Vasilios Zarikas, Stavros Poulopoulos

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%.

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Paper Citation


in Harvard Style

Zholdasbayeva M., Zarikas V. and Poulopoulos S. (2020). Bayesian Networks based Policy Making in the Renewable Energy Sector.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 453-462. DOI: 10.5220/0008925004530462


in Bibtex Style

@conference{icaart20,
author={Moldir Zholdasbayeva and Vasilios Zarikas and Stavros Poulopoulos},
title={Bayesian Networks based Policy Making in the Renewable Energy Sector},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={453-462},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008925004530462},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Bayesian Networks based Policy Making in the Renewable Energy Sector
SN - 978-989-758-395-7
AU - Zholdasbayeva M.
AU - Zarikas V.
AU - Poulopoulos S.
PY - 2020
SP - 453
EP - 462
DO - 10.5220/0008925004530462