IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v390y2025ics0306261925005422.html
   My bibliography  Save this article

Improved surrogate modeling for multi-energy system design: Model architecture, sampling and scaling choices

Author

Listed:
  • Lédée, François
  • Crawford, Curran
  • Evins, Ralph

Abstract

Multi-energy systems (MES) are a key concept for developing more sustainable energy systems, but optimizing their design is computationally burdensome. This paper explores the development of machine-learning (ML) based surrogate models for the optimal design of MES. Surrogates are simple models, often ML-based, used to approximate detailed simulations, in this case MES design optimizations. These models provide instant responses, enabling fast comparisons and explorations of trade-offs between design variables. No related work proposes an ML procedure tailored to properties of the MES design application. Most related works use surrogates to predict system cost and other objectives. However, few works have used them to directly predict the optimal system design, and those that do show poor performance.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:390:y:2025:i:c:s0306261925005422
    DOI: 10.1016/j.apenergy.2025.125812
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925005422
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.125812?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. Gabrielli, Paolo & Gazzani, Matteo & Martelli, Emanuele & Mazzotti, Marco, 2018. "Optimal design of multi-energy systems with seasonal storage," Applied Energy, Elsevier, vol. 219(C), pages 408-424.
    2. Kotzur, Leander & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "Time series aggregation for energy system design: Modeling seasonal storage," Applied Energy, Elsevier, vol. 213(C), pages 123-135.
    3. 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).
    4. Elomari, Youssef & Mateu, Carles & Marín-Genescà, M. & Boer, Dieter, 2024. "A data-driven framework for designing a renewable energy community based on the integration of machine learning model with life cycle assessment and life cycle cost parameters," Applied Energy, Elsevier, vol. 358(C).
    5. Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
    6. Murray, Portia & Carmeliet, Jan & Orehounig, Kristina, 2020. "Multi-Objective Optimisation of Power-to-Mobility in Decentralised Multi-Energy Systems," Energy, Elsevier, vol. 205(C).
    7. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    8. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2020. "Introducing reinforcement learning to the energy system design process," Applied Energy, Elsevier, vol. 262(C).
    9. Westermann, Paul & Welzel, Matthias & Evins, Ralph, 2020. "Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones," Applied Energy, Elsevier, vol. 278(C).
    10. 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.
    11. Thrampoulidis, Emmanouil & Hug, Gabriela & Orehounig, Kristina, 2023. "Approximating optimal building retrofit solutions for large-scale retrofit analysis," Applied Energy, Elsevier, vol. 333(C).
    12. Marquant, Julien F. & Evins, Ralph & Bollinger, L. Andrew & Carmeliet, Jan, 2017. "A holarchic approach for multi-scale distributed energy system optimisation," Applied Energy, Elsevier, vol. 208(C), pages 935-953.
    13. Zahra Jahangiri & Mackenzie Judson & Kwang Moo Yi & Madeleine McPherson, 2023. "A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System," Energies, MDPI, vol. 16(3), pages 1-21, January.
    14. Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
    15. Lédée, François & Evins, Ralph, 2024. "A comparison of 4th and 5th generation thermal networks with energy hub," Energy, Elsevier, vol. 311(C).
    16. Evins, Ralph, 2015. "Multi-level optimization of building design, energy system sizing and operation," Energy, Elsevier, vol. 90(P2), pages 1775-1789.
    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. Lédée, François & Evins, Ralph, 2024. "A comparison of 4th and 5th generation thermal networks with energy hub," Energy, Elsevier, vol. 311(C).
    2. Mavromatidis, Georgios & Petkov, Ivalin, 2021. "MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems," Applied Energy, Elsevier, vol. 288(C).
    3. Petkov, Ivalin & Gabrielli, Paolo & Spokaite, Marija, 2021. "The impact of urban district composition on storage technology reliance: trade-offs between thermal storage, batteries, and power-to-hydrogen," Energy, Elsevier, vol. 224(C).
    4. Kiani-Moghaddam, Mohammad & Soltani, Mohsen N. & Kalogirou, Soteris A. & Arabkoohsar, Ahmad, 2025. "A review of optimization efforts on neighborhood-level energy hubs," Energy, Elsevier, vol. 317(C).
    5. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2020. "Introducing reinforcement learning to the energy system design process," Applied Energy, Elsevier, vol. 262(C).
    6. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    7. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    8. Reveron Baecker, Beneharo & Candas, Soner, 2022. "Co-optimizing transmission and active distribution grids to assess demand-side flexibilities of a carbon-neutral German energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    9. van der Heijde, Bram & Vandermeulen, Annelies & Salenbien, Robbe & Helsen, Lieve, 2019. "Representative days selection for district energy system optimisation: a solar district heating system with seasonal storage," Applied Energy, Elsevier, vol. 248(C), pages 79-94.
    10. Wakui, Tetsuya & Akai, Kazuki & Yokoyama, Ryohei, 2022. "Shrinking and receding horizon approaches for long-term operational planning of energy storage and supply systems," Energy, Elsevier, vol. 239(PD).
    11. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2019. "A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin," Applied Energy, Elsevier, vol. 241(C), pages 331-361.
    12. Wirtz, Marco & Kivilip, Lukas & Remmen, Peter & Müller, Dirk, 2020. "5th Generation District Heating: A novel design approach based on mathematical optimization," Applied Energy, Elsevier, vol. 260(C).
    13. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
    14. Svitnič, Tibor & Sundmacher, Kai, 2022. "Renewable methanol production: Optimization-based design, scheduling and waste-heat utilization with the FluxMax approach," Applied Energy, Elsevier, vol. 326(C).
    15. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    16. Dong, Haoxin & Shan, Zijing & Zhou, Jianli & Xu, Chuanbo & Chen, Wenjun, 2023. "Refined modeling and co-optimization of electric-hydrogen-thermal-gas integrated energy system with hybrid energy storage," Applied Energy, Elsevier, vol. 351(C).
    17. Wang, Jing & Kang, Lixia & Liu, Yongzhong, 2022. "A multi-objective approach to determine time series aggregation strategies for optimal design of multi-energy systems," Energy, Elsevier, vol. 258(C).
    18. Dal Cin, Enrico & Carraro, Gianluca & Volpato, Gabriele & Lazzaretto, Andrea & Tsatsaronis, George, 2025. "DOMES: A general optimization method for the integrated design of energy conversion, storage and networks in multi-energy systems," Applied Energy, Elsevier, vol. 377(PD).
    19. Damien Picard & Lieve Helsen, 2018. "Economic Optimal HVAC Design for Hybrid GEOTABS Buildings and CO 2 Emissions Analysis," Energies, MDPI, vol. 11(2), pages 1-19, February.
    20. Hilbers, Adriaan P. & Brayshaw, David J. & Gandy, Axel, 2023. "Reducing climate risk in energy system planning: A posteriori time series aggregation for models with storage," Applied Energy, Elsevier, vol. 334(C).

    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:390:y:2025:i:c:s0306261925005422. 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.