Model Prediction XGBoost for Perspective of Closing Cernavoda Reactor 1 and the Impact on the Romanian Energy System

Crețulescu Andreea () and Popescu Crețulescu Alexandru ()
Additional contact information
Crețulescu Andreea: Bucharest, Romania
Popescu Crețulescu Alexandru: Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2024, vol. 18, issue 1, 2871-2881

Abstract: Employing artificial intelligence (AI) and machine learning (ML) technologies to analyze the progression of energy markets is imperative, as these methodologies facilitate the modeling of diverse domain-specific data to forecast developments in an exceedingly volatile market. Utilizing machine learning algorithms to model data concerning the Romanian energy sector proves beneficial, particularly in circumstances where significant changes necessitate adaptation to internal consumption demands, while mitigating substantial fluctuations in electricity prices. Such fluctuations can profoundly impact crucial national economic indicators.. In this paper, we aim to simulate such a situation for the moment when Romania will be obliged to shut down and modernize Reactor 1 at Cernavodă due to reaching the standard operating time for the CANDU model. We will use an XGBoost for Regression algorithm in which we will input data about the national energy system (consumption, energy quantities according to the energy system's production structure by resource types) from the period 2020-2023, and then we will simulate the shutdown of the reactor to analyze how the national energy market is influenced. Our findings not only reveal the indispensable role of predictive analysis through ML/AI models in anticipating market changes, but also provide practical insights for decision-makers aiming to optimize the national energy system so that when Reactor 1 at Cernavodă is shut down, the domestic market is affected as little as possible, considering that this reactor has supplied the national system with approximately 700 MWe/h over the past 20 years.

Keywords: dataset; artificial intelligence; nuclear; energy; machine learning; xgboost; algorithm; Cernavodă Reactor 1 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/picbe-2024-0238 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:18:y:2024:i:1:p:2871-2881:n:1033

DOI: 10.2478/picbe-2024-0238

Access Statistics for this article

Proceedings of the International Conference on Business Excellence is currently edited by Alina Mihaela Dima

More articles in Proceedings of the International Conference on Business Excellence from Sciendo
Bibliographic data for series maintained by Peter Golla ().