Agent-Based Energy Market Modeling with Machine Learning and Econometric Forecasting for the Net-Zero Emissions Transition

Burak Gokce () and Gulgun Kayakutlu
Additional contact information
Burak Gokce: Energy Science and Technology, Energy Institute, Istanbul Technical University, 34469 Istanbul, Türkiye
Gulgun Kayakutlu: Energy Science and Technology, Energy Institute, Istanbul Technical University, 34469 Istanbul, Türkiye

Energies, 2025, vol. 18, issue 21, 1-27

Abstract: The transition of Türkiye’s energy market toward net-zero emissions by 2053 requires modeling approaches capable of capturing complex interactions and long-term uncertainties. In this study, a long-term agent-based modeling (ABM) framework was developed, integrating econometric demand forecasting with a seasonal autoregressive integrated moving average (SARIMA) model and machine learning (ML)-based day-ahead market (DAM) price prediction. Of the ML models tested, CatBoost achieved the highest accuracy, outperforming XGBoost and Random Forest, and supported investment analysis through net present value (NPV) calculations. The framework represents major market actors—including generation units, investors, and the market operator—while also incorporating the impact of Türkiye’s first nuclear power plant (NPP) under construction and the potential introduction of a carbon emissions trading scheme (ETS). All model components were validated against historical data, confirming robust forecasting and market replication performance. Hourly simulations were conducted until 2053 under alternative policy and demand scenarios. The results show that renewable generation expands steadily, led by onshore wind and solar photovoltaic (PV), while nuclear capacity, ETS implementation, and demand assumptions significantly reshape prices, generation mix, and carbon emissions. The nuclear plant lowers market prices, whereas an ETS substantially raises them, with both policies contributing to emission reductions. These scenario results were connected to actionable policy recommendations, outlining how renewable expansion, ETS design, nuclear development, and energy efficiency measures can jointly support Türkiye’s 2053 net-zero target. The proposed framework provides an ex-ante decision-support framework for policymakers, investors, and market participants, with future extensions that can include other energy markets, storage integration, and enriched scenario design.

Keywords: agent-based modeling; machine learning; econometric forecasting; energy markets; electricity markets; carbon emissions trading (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/21/5655/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/21/5655/ (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:gam:jeners:v:18:y:2025:i:21:p:5655-:d:1781559

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().