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

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  • 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
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