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Processes
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Advanced Processes for Sustainable Energy Conversion and Utilization
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Advanced Processes for Sustainable Energy Conversion and Utilization

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 3145

Editor

Prof. Dr. Erol Kurt

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Gazi University, Ankara 06560, Turkey
Interests: energy system design; energy system implementation; electromagnetic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable tools for energy conversion are becoming increasingly important across various energy sub-fields worldwide. In particular, the processes involved in solar photovoltaic systems; consentrated solar systems; wind turbines and their alternators; harvesting devices working under mechanical, electromagnetic, thermal, and optical stresses; and battery technologies with invertor/convertor circuitries have garnered significant interest. It is widely known that even a 1% increase in energy conversion efficiency can lead to substantial cost savings for the gloabl energy sector. Therefore, it is imperative that we promote sustainable efforts on energy conversion mechanisms. In this context, the scope of our Special Issue includes innovative design and prototype production in materials science, as well as improvements in the efficiency of auxiliary equipment used during energy conversion processes. Additionally, contributions focused on enhancing the efficiency of chemical reactions in any fuel-cell or battery system are also welcome for submission to this Special Issue.

Prof. Dr. Erol Kurt
Guest Editor

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes 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 2400 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

  • transformer
  • harvester
  • solar
  • photovoltaic
  • crystal growth
  • conversion
  • energy
  • battery
  • invertor
  • convertor
  • wind
  • fuel-cell
  • generator

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Published Papers (4 papers)

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Research

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46 pages, 4459 KB  
Article
Short-Term Electricity Demand Forecasting: A Comparative Evaluation of Models Based on Performance Criteria and Future Research Directions
by Anderson Sebastian Torres-Sánchez, Álvaro Jaramillo-Duque and Walter M. Villa-Acevedo
Processes 2026, 14(14), 2265; https://doi.org/10.3390/pr14142265 - 11 Jul 2026
Viewed by 332
Abstract
Short-term electricity demand forecasting is a critical enabler of the secure and efficient operation of modern power systems, particularly amid increasing renewable energy integration, smart grid expansion, and the broader energy transition. This paper presents a rigorous comparative analysis of electricity demand forecasting [...] Read more.
Short-term electricity demand forecasting is a critical enabler of the secure and efficient operation of modern power systems, particularly amid increasing renewable energy integration, smart grid expansion, and the broader energy transition. This paper presents a rigorous comparative analysis of electricity demand forecasting models, encompassing statistical methods, Machine Learning (ML), Deep Learning (DL), and hybrid architectures. A structured taxonomy is proposed to classify models according to their methodological family, application horizon, and data requirements, thereby providing a unified reference framework for researchers and energy-sector practitioners. Models are evaluated using a multi-criteria framework comprising accuracy, robustness, scalability, interpretability, computational cost, and the capacity to handle exogenous variables. The analysis identifies critical research gaps, including the limited integration of probabilistic forecasting into operational contexts and the absence of standardized evaluation protocols under real-world conditions. Future research directions are outlined, with particular emphasis on uncertainty quantification, adaptive learning strategies, and hierarchical forecast coherence in systems with high penetration of distributed energy resources. Full article
(This article belongs to the Special Issue Advanced Processes for Sustainable Energy Conversion and Utilization)
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33 pages, 9010 KB  
Article
Reduced-Order Modeling of Transient Events in Data Centers Using Dimensionality Reduction Techniques
by Julio Cesar Ramírez Acero, Ricardo Isaza-Ruget and Javier Rosero-García
Processes 2026, 14(10), 1665; https://doi.org/10.3390/pr14101665 - 21 May 2026
Viewed by 846
Abstract
The present paper proposes a methodology for the analysis and modelling of transient events in a data center based on real-world high-resolution voltage and current measurements. The proposed approach includes the identification of relevant events, temporal segmentation, multivariate representation, and the application of [...] Read more.
The present paper proposes a methodology for the analysis and modelling of transient events in a data center based on real-world high-resolution voltage and current measurements. The proposed approach includes the identification of relevant events, temporal segmentation, multivariate representation, and the application of dimensionality reduction techniques to obtain compact representations of the observed dynamics. A total of eight representative transient events were identified in the available dataset. These events were characterized by short-duration disturbances of moderate magnitude, which is consistent with the operation of highly reliable infrastructures. Three main methods were evaluated: PCA/POD, Kernel PCA, and Autoencoder. The results show that all three approaches are capable of reconstructing the event dynamics with low reconstruction errors, suggesting the presence of a low-dimensional structure in the analyzed data. Among the evaluated methods, PCA/POD provided the best balance between compactness, interpretability, and computational efficiency, while Kernel PCA and Autoencoder offered advantages for representing nonlinear behaviors. The results provide case-study evidence on the feasibility of constructing reduced-order representations for the analysis and monitoring of transient events in data centers under limited-data conditions. Full article
(This article belongs to the Special Issue Advanced Processes for Sustainable Energy Conversion and Utilization)
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27 pages, 6058 KB  
Article
A Dynamic Energy Management Algorithm for Battery–Ultracapacitor-Based UPS Systems
by Yagmur Kircicek and Hakan Akca
Processes 2025, 13(12), 3762; https://doi.org/10.3390/pr13123762 - 21 Nov 2025
Viewed by 1129
Abstract
This study presents a dynamic energy management algorithm (DEMA) designed for hybrid battery–ultracapacitor systems in uninterruptible power supply (UPS) applications. The proposed algorithm aims to enhance power reliability and extend battery life by dynamically coordinating energy flow between the battery and ultracapacitor under [...] Read more.
This study presents a dynamic energy management algorithm (DEMA) designed for hybrid battery–ultracapacitor systems in uninterruptible power supply (UPS) applications. The proposed algorithm aims to enhance power reliability and extend battery life by dynamically coordinating energy flow between the battery and ultracapacitor under various operating modes. A single-phase UPS system was modeled and simulated in MATLAB/Simulink (Matlab R2025a version), and subsequently validated through experimental tests using an energy analyzer and an oscilloscope. The DEMA identifies and manages five operating modes, ensuring smooth transitions between grid-connected and backup states. During sudden load variations, particularly at a 1500 W step change, the ultracapacitor effectively supports the battery by supplying transient power, thereby reducing current stress and preventing deep discharge. Both simulation and experimental results confirm that the proposed algorithm maintains stable DC bus voltage, improves dynamic response, and achieves optimal energy utilization across all modes. The developed hybrid UPS control approach demonstrates high reliability and can be effectively implemented in critical load systems requiring uninterrupted power and enhanced battery longevity. Full article
(This article belongs to the Special Issue Advanced Processes for Sustainable Energy Conversion and Utilization)
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Review

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30 pages, 1335 KB  
Review
Trustworthy Digital Auxiliary Processes for Sustainable Energy Utilization: A Targeted Process-Oriented Review and Conceptual Evidence-to-Review Framework
by Jihoon Moon
Processes 2026, 14(16), 2594; https://doi.org/10.3390/pr14162594 - 14 Aug 2026
Abstract
Sustainable energy systems increasingly rely on digital auxiliary processes that convert telemetry, forecasts, diagnostic evidence, and operating rules into reviewable information for authorized human decision-makers. This targeted review develops a transparent and traceable conceptual evidence-to-review framework for photovoltaic, wind, storage, fuel-cell, and converter-coupled [...] Read more.
Sustainable energy systems increasingly rely on digital auxiliary processes that convert telemetry, forecasts, diagnostic evidence, and operating rules into reviewable information for authorized human decision-makers. This targeted review develops a transparent and traceable conceptual evidence-to-review framework for photovoltaic, wind, storage, fuel-cell, and converter-coupled systems. The coded review pool comprised 100 sources, including 75 analytical and 25 contextual sources; 10 additional references supported methodological, technical, and case-specific context and were not included in thematic coding. A six-stage thematic analysis, supported by a revision-stage coding audit, identified five analytical layers: data-quality control, forecasting and uncertainty, explanation diagnostics, constrained evidence briefing, and accountable human review. These layers were translated into an eight-stage advisory architecture that separates evidence preparation, validation, briefing, and review from the authorized control plane. No direct large-language-model-to-controller or model-to-asset path is permitted. An illustrative Jeju scenario traces one evidence record through the architecture and shows how safety conditions, evidence admissibility, and procedural authority lead to CHECK, HOLD, or ESCALATE outcomes. The review concludes that trustworthy digital support requires traceable evidence, deterministic safety and rule boundaries, and accountable human authorization. Full article
(This article belongs to the Special Issue Advanced Processes for Sustainable Energy Conversion and Utilization)
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