IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v394y2025ics0306261925008761.html

Explorative application of discrete Bayesian networks as surrogate models for energy systems analysis

Author

Listed:
  • Kelk, Rainer
  • Podofillini, Luca
  • Dang, Vinh N.
  • Panos, Evangelos

Abstract

This work investigates data-based discrete Bayesian Belief Networks (BBNs) as surrogate energy system models for result analysis and interactive analyses, such as what-if analyses. A simplified version of the Swiss TIMES (STEM) model, referred to as STEM-lite, is used for demonstration. A method to optimize the BBN model is devised, based on performance metrics related to the accuracy of the BBN predictions, calculated over data records unseen by the BBN in the training phase. Further validation of the BBN on a set of seven scenarios yielded an average relative error below 2 %, suggesting adequate performance as surrogate model. The application of the surrogate BBN model is demonstrated to highlight its benefits, which include enabling interactive analysis (supported by the visualization of key variables, their relationships and interactions), fast and intuitive uncertainty propagation, and support for goal-driven analysis (backward reasoning from outcomes to the inputs that produce these outcomes). The surrogate BBN presented here was developed to elaborate the methods for constructing, validating, and using BBN models for energy systems analysis and to demonstrate the benefits of such a model; at this stage, this model is not intended for energy systems and economics policy discussions. For practical applications, future work is needed to reduce the number of data records to construct the BBN, to introduce the option to treat the time dependence of the input variables, and to allow for larger BBN models (involving more variables) that reflect the increasing complexity of energy systems.

Suggested Citation

  • Kelk, Rainer & Podofillini, Luca & Dang, Vinh N. & Panos, Evangelos, 2025. "Explorative application of discrete Bayesian networks as surrogate models for energy systems analysis," Applied Energy, Elsevier, vol. 394(C).
    • Handle: RePEc:eee:appene:v:394:y:2025:i:c:s0306261925008761
    DOI: 10.1016/j.apenergy.2025.126146
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925008761
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.126146?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Zio, Enrico & Mustafayeva, Maryam & Montanaro, Andrea, 2022. "A Bayesian Belief Network Model for the Risk Assessment and Management of Premature Screen-Out during Hydraulic Fracturing," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Panos, Evangelos & Glynn, James & Kypreos, Socrates & Lehtilä, Antti & Yue, Xiufeng & Ó Gallachóir, Brian & Daniels, David & Dai, Hancheng, 2023. "Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector," Energy Policy, Elsevier, vol. 180(C).
    3. Ali, Shahid & Stewart, Rodney A. & Sahin, Oz & Vieira, Abel Silva, 2024. "Spatial bayesian approach for socio-economic assessment of pumped hydro storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    5. Li, Pei-Hao & Zamanipour, Behzad & Keppo, Ilkka, 2024. "Revealing technological entanglements in uncertain decarbonisation pathways using bayesian networks," Energy Policy, Elsevier, vol. 193(C).
    6. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "Machine learning methods to assist energy system optimization," Applied Energy, Elsevier, vol. 243(C), pages 191-205.
    7. Fodstad, Marte & Crespo del Granado, Pedro & Hellemo, Lars & Knudsen, Brage Rugstad & Pisciella, Paolo & Silvast, Antti & Bordin, Chiara & Schmidt, Sarah & Straus, Julian, 2022. "Next frontiers in energy system modelling: A review on challenges and the state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    8. Vincenzina Vitale & Flaminia Musella & Paola Vicard & Valentina Guizzi, 2020. "Modelling an energy market with Bayesian networks for non-normal data," Computational Management Science, Springer, vol. 17(1), pages 47-64, January.
    9. Babak Fazelabdolabadi, 2019. "A hybrid Bayesian-network proposition for forecasting the crude oil price," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-21, December.
    10. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    11. Li, Francis G.N. & Bataille, Chris & Pye, Steve & O'Sullivan, Aidan, 2019. "Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?," Applied Energy, Elsevier, vol. 239(C), pages 991-1002.
    12. Yaxin Shi & Suning Liu & Haiyun Shi, 2022. "Analysis of the Water-Food-Energy Nexus and Water Competition Based on a Bayesian Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3349-3366, July.
    13. Shi, Huiting & Chai, Jian & Lu, Quanying & Zheng, Jiali & Wang, Shouyang, 2022. "The impact of China's low-carbon transition on economy, society and energy in 2030 based on CO2 emissions drivers," Energy, Elsevier, vol. 239(PD).
    14. Borunda, Mónica & Jaramillo, O.A. & Reyes, Alberto & Ibargüengoytia, Pablo H., 2016. "Bayesian networks in renewable energy systems: A bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 32-45.
    15. Prina, Matteo Giacomo & Dallapiccola, Mattia & Moser, David & Sparber, Wolfram, 2024. "Machine learning as a surrogate model for EnergyPLAN: Speeding up energy system optimization at the country level," Energy, Elsevier, vol. 307(C).
    16. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
    17. Hernández-Cedeño, Isaac & Nelson, Pamela F. & Anglés-Hernández, Marisol, 2021. "Social and environmental conflict analysis on energy projects: Bayesian predictive network approach," Energy Policy, Elsevier, vol. 157(C).
    18. Morris, Peter & Vine, Desley & Buys, Laurie, 2015. "Application of a Bayesian Network complex system model to a successful community electricity demand reduction program," Energy, Elsevier, vol. 84(C), pages 63-74.
    19. Zhang, Mingming & Zhou, Simei & Wang, Qunwei & Liu, Liyun & Zhou, Dequn, 2023. "Will the carbon neutrality target impact China's energy security? A dynamic Bayesian network model," Energy Economics, Elsevier, vol. 125(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maticka, Martin J. & Mahmoud, Thair S., 2025. "Bayesian Belief Networks: Redefining wholesale electricity price modelling in high penetration non-firm renewable generation power systems," Renewable Energy, Elsevier, vol. 239(C).
    2. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    3. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    4. Thangjam, Aditya & Jaipuria, Sanjita & Dadabada, Pradeep Kumar, 2023. "Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks," Applied Energy, Elsevier, vol. 333(C).
    5. Lédée, François & Crawford, Curran & Evins, Ralph, 2025. "Improved surrogate modeling for multi-energy system design: Model architecture, sampling and scaling choices," Applied Energy, Elsevier, vol. 390(C).
    6. Ha, Le Thanh, 2024. "Dynamic spill-over influences of FinTech innovation development on renewable energy volatility during the time of war in pandemic: A novel insight from a wavelet model," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 515-529.
    7. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    8. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    9. Li, Pei-Hao & Zamanipour, Behzad & Keppo, Ilkka, 2024. "Revealing technological entanglements in uncertain decarbonisation pathways using bayesian networks," Energy Policy, Elsevier, vol. 193(C).
    10. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    11. Zheng, Li & Abbasi, Kashif Raza & Salem, Sultan & Irfan, Muhammad & Alvarado, Rafael & Lv, Kangjuan, 2022. "How technological innovation and institutional quality affect sectoral energy consumption in Pakistan? Fresh policy insights from novel econometric approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    12. Shuangshuang Fan & Yichao Li & William Mbanyele & Xiufeng Lai, 2025. "Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1231-1264, March.
    13. Rosendal, M. & Janin, J. & Heggarty, T. & Pisinger, D. & Bramstoft, R. & Münster, M., 2025. "The benefits and challenges of soft-linking investment and operational energy system models," Applied Energy, Elsevier, vol. 385(C).
    14. Zhu, Jianhua & Peng, Yan & Gong, Zhuping & Sun, Yanming & Lai, Chaoan & Wang, Qing & Zhu, Xiaojun & Gan, Zhongxue, 2019. "Dynamic analysis of SNG and PNG supply: The stability and robustness view #," Energy, Elsevier, vol. 185(C), pages 717-729.
    15. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    16. Gao, Daquan & Li, Songsong & Tian, Zhihong, 2025. "Geopolitical risk, energy market volatility, and corporate energy dependence: The role of green Total factor productivity and decentralized top management team network," Energy Economics, Elsevier, vol. 148(C).
    17. Ercan Köse & Sevil Kutlu Kaynar, 2025. "Energy Demand Forecasting and Policy Development in Turkey," Energies, MDPI, vol. 18(13), pages 1-31, June.
    18. Shirizadeh, Behrang & Quirion, Philippe, 2022. "The importance of renewable gas in achieving carbon-neutrality: Insights from an energy system optimization model," Energy, Elsevier, vol. 255(C).
    19. Peng, Benhong & Zhao, Yinyin & Elahi, Ehsan & Wan, Anxia, 2023. "Can third-party market cooperation solve the dilemma of emissions reduction? A case study of energy investment project conflict analysis in the context of carbon neutrality," Energy, Elsevier, vol. 264(C).
    20. Erna Farina Mohamed & Azlina Abdullah & Amar Hisham Jaaffar, 2024. "Energy Efficiency in Developing Countries: A Systematic Review of Current Findings and Directions towards a Net Zero Economy," International Journal of Energy Economics and Policy, Econjournals, vol. 14(5), pages 495-508, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:394:y:2025:i:c:s0306261925008761. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.