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AI-Driven Modeling and Optimization for Industrial Energy Systems
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AI-Driven Modeling and Optimization for Industrial Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 25 September 2026 | Viewed by 2473

Special Issue Editors

Prof. Dr. Lianlei Lin

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: renewable and sustainable energy; intelligent modeling methods and application technologies; wind power grid integration; industrial energy systems
Dr. Shengyu Tao

E-Mail Website
Guest Editor
Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Interests: batteries; energy systems; AI for science; AI for sustainability; controls
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial energy systems—spanning manufacturing, chemical processing, and power generation—are characterized by complex dynamics, strong coupling, and high energy demand. With the rise in artificial intelligence (AI), data-driven modeling and intelligent optimization have become essential tools to enhance operational efficiency, reduce emissions, and support sustainable industrial development. AI-driven methods, including deep learning, reinforcement learning, and physics-informed neural networks, can model nonlinear system behaviors, predict process states, and enable multi-objective optimization under uncertainty. Integrating these approaches with domain knowledge fosters adaptive, interpretable, and high-performance energy solutions for the next generation of smart industries. This Special Issue aims to present recent advances in AI-based modeling and optimization for industrial energy systems. It welcomes original research and comprehensive reviews that bridge data-driven learning with physical insight, advancing the intelligent design, operation, and management of industrial energy infrastructures.

Topics:

  • AI-based modeling and simulation of industrial energy systems.
  • Deep learning for process optimization and energy forecasting.
  • Reinforcement learning for adaptive energy management.
  • Physics-informed and hybrid AI frameworks for system modeling.
  • Multi-objective optimization for efficiency and emission reduction.
  • Intelligent fault detection and predictive maintenance.
  • Digital twins for optimization and decision support in energy systems.

Prof. Dr. Lianlei Lin
Dr. Shengyu Tao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • industrial energy systems
  • AI-driven modeling
  • intelligent optimization
  • hybrid modeling

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Published Papers (3 papers)

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Research

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25 pages, 1610 KB  
Article
Supervised Imitation Learning for Optimal Setpoint Trajectory Prediction in Energy Management Under Dynamic Electricity Pricing
by Philipp Wohlgenannt, Vinzent Vetter, Lukas Moosbrugger, Mohan Kolhe, Elias Eder and Peter Kepplinger
Energies 2026, 19(6), 1459; https://doi.org/10.3390/en19061459 - 13 Mar 2026
Viewed by 603
Abstract
Energy management systems operating under dynamic electricity pricing require fast and cost-optimal control strategies for flexible loads. Mixed-integer linear programming (MILP) can compute theoretically optimal control trajectories but is computationally expensive and typically relies on accurate load forecasts, limiting its practical real-time applicability. [...] Read more.
Energy management systems operating under dynamic electricity pricing require fast and cost-optimal control strategies for flexible loads. Mixed-integer linear programming (MILP) can compute theoretically optimal control trajectories but is computationally expensive and typically relies on accurate load forecasts, limiting its practical real-time applicability. This paper proposes a supervised imitation learning (IL) framework that learns optimal setpoint trajectories for a conventional proportional (P) controller directly from electricity price signals and temporal features, thereby eliminating the need for explicit load forecasting. The learned model predicts setpoint trajectories in an open-loop manner, while a lower-level P controller ensures stable closed-loop operation within a two-stage control architecture. The approach is validated in an industrial case study involving load shifting of a refrigeration system under dynamic electricity pricing and benchmarked against MILP optimization, reinforcement learning (RL), heuristic strategies, and various machine learning models. The MILP solution achieves a cost reduction of 21.07% and represents a theoretical upper bound under perfect information. The proposed Transformer model closely approximates this optimum, achieving 19.33% cost reduction while enabling real-time inference. Overall, the results demonstrate that the proposed supervised IL approach can achieve near-optimal control performance with substantially reduced computational effort for real-time energy management applications. Full article
(This article belongs to the Special Issue AI-Driven Modeling and Optimization for Industrial Energy Systems)
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41 pages, 1911 KB  
Article
A Physics-Informed Combinatorial Digital Twin for Value-Optimized Production of Petroleum Coke
by Vladimir V. Bukhtoyarov, Alexey A. Gorodov, Natalia A. Shepeta, Ivan S. Nekrasov, Oleg A. Kolenchukov, Svetlana S. Kositsyna and Artem Y. Mikhaylov
Energies 2026, 19(2), 451; https://doi.org/10.3390/en19020451 - 16 Jan 2026
Viewed by 603
Abstract
Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results. This study introduces a physics-informed combinatorial digital twin for value-optimized coking, aimed at improving energy [...] Read more.
Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results. This study introduces a physics-informed combinatorial digital twin for value-optimized coking, aimed at improving energy efficiency and environmental performance through adaptive quality forecasting. The approach builds a modular library of 32 candidate equations grouped into eight quality parameters and links them via cross-parameter dependencies. A two-level optimization scheme is applied: a genetic algorithm selects the best model combination, while a secondary loop tunes parameters under a multi-objective fitness function balancing accuracy, interpretability, and computational cost. Validation on five clustered operating regimes (industrial patterns augmented with noise-perturbed synthetic data) shows that optimal model ensembles outperform single best models, achieving typical cluster errors of ~7–13% NMAE. The developed digital twin framework enables accurate prediction of coke quality parameters that are critical for its energy applications, such as volatile matter and sulfur content, which serve as direct proxies for estimating the net calorific value and environmental footprint of coke as a fuel. Full article
(This article belongs to the Special Issue AI-Driven Modeling and Optimization for Industrial Energy Systems)
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Review

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50 pages, 6593 KB  
Review
Current Applications and Future Prospects of Deep Reinforcement Learning in Energy Management for Hybrid Power Systems
by Zhao Li, Wuqiang Long and Hua Tian
Energies 2026, 19(9), 2216; https://doi.org/10.3390/en19092216 - 3 May 2026
Viewed by 777
Abstract
Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system’s overall [...] Read more.
Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system’s overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge. Full article
(This article belongs to the Special Issue AI-Driven Modeling and Optimization for Industrial Energy Systems)
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