[2604.01308] An Online Machine Learning Multi-resolution Optimization Framework for Energy System Design Limit of Performance Analysis

Computer Science > Machine Learning arXiv:2604.01308 (cs) [Submitted on 1 Apr 2026] Title:An Online Machine Learning Multi-resolution Optimization Framework for Energy System Design Limit of Performance Analysis Authors:Oluwamayowa O. Amusat, Luka Grbcic, Remi Patureau, M. Jibran S. Zuberi, Dan Gunter, Michael Wetter View a PDF of the paper titled An Online Machine Learning Multi-resolution Optimization Framework for Energy System Design Limit of Performance Analysis, by Oluwamayowa O. Amusat and 5 other authors View PDF HTML (experimental) Abstract:Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to high-fidelity dynamic operation. However, model mismatch across fidelities obscures the sources of performance loss and complicates the quantification of architecture-to-operation performance gaps. We propose an online, machine-learning-accelerated multi-resolution optimization framework that estimates an architecture-specific upper bound on achievable performance while minimizing expensive high-fidelity model evaluations. We demonstrate the approach on a pilot energy system supplying a 1 MW industrial heat load. First, we solve a multi-objective architecture optimization to select the system configuration and component capacities. We then develop an machine learning (ML)-accelerated multi-resolution, receding-horizon optimal control strategy that appro...