Artificial Intelligence for Sustainable Energy Systems: Smart Grids, Homeostatic Control, and Distributed Resource Optimization

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

Deadline for manuscript submissions: 30 January 2026 | Viewed by 3316

Special Issue Editors


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Guest Editor
Faculty of Engineering, Finis Terrae University, Av. Pedro de Valdivia 1509, Providencia, Santiago 7501014, Chile
Interests: enterprise sustainability; energy sustainability and sustainable hybrid energy systems (SHES) for on-grid distributed generation; energy homeostasis management/homeostatic control and smart grid technology advancement
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Guest Editor
Department of Electrical Engineering, Parala Maharaja Engineering College, Odisha 761003, India
Interests: renewable energy automation; distribution systems; energy forecasting; machine learning; control and optimization; real-time monitoring

Special Issue Information

Dear Colleagues,

We are navigating very challenging times, where new and radical changes in technology are arising on all fronts. In a world of hyperconnectivity, ultra-fast sensing networks equipped with IoT, ubiquitous cloud computing integrated with various AI technologies, and enormous loads of data processing which increase every year, the need for ever greater amounts of electricity is growing and becoming a pressing constraint on industries in general. As a result, we require more reliable, resilient, and sustainable high-power transmission and distribution networks. To help advance this fast-paced, ambitious agenda and contribute to the modernization of electric power networks, researchers, like ourselves, and industry pundits, in general, are called to support key areas of the development of the Smart Grid and Sustainable Energy Systems (SES) agenda, such as Energy Homeostasis Management and Distributed Resources Optimization based on AI deployment. This agenda calls for new applications of technologies with an ever-present system engineering approach, where AI and energy management take the center stage. However, along with the Smart Grid and SES agenda, service quality issues are arising as a consequence of small- and medium-sized means of distributed generation connecting to the grid, as permitted by law. Hence, in addition to leveraging rapidly configurable, scalable, and deployable distributed energy resources (DERs) such as the microgrid and employing renewables, flexible loads, AI, IOT, and smart energy management systems for electric distribution companies, the issue of how to intervene in the grid and resolve service quality issues swiftly before the problem escalates remains. DG systems such as the Virtual Power Plant (VPP) play an important role in the integration of DER into electric power distribution networks so as to provide critical assistance to the grid in a particular location whenever necessary. Thus, this Special Issue is dedicated to these pressing issues, as we face an ever-growing demand for electric power, and aims to address potential regulatory changes and new, more advanced engineering solutions to negative impacts on the grid's service quality standards and worse yet—blackouts. This Special Issue is therefore dedicated to this important area of research, not only due to its direct link to the advancement of the Smart Grid and Sustainable Energy Systems, but also because of its economic and regulatory ramifications, which undoubtedly affect every sector of countries’ economies, in particular, the energy supply and the electric power distribution sector.

Dr. Fernando Yanine
Prof. Dr. Sarat Kumar Sahoo
Guest Editors

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Keywords

  • smart grid
  • microgrid on-grid
  • small means of distributed generation (PMGD)
  • electrical energy distribution
  • distributed energy resources (DER)
  • service quality standards
  • energy homeostasis
  • sustainable energy systems (SES)

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Published Papers (2 papers)

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Research

17 pages, 896 KB  
Article
A Method for Load Forecasting of Distribution Transformers Based on Parameter-Efficient Fine-Tuning of Large Language Models in Power System
by Weijian Zhang, Shanfeng Liu, Miaomiao Li, Hua Bao, Shaoguang Yuan and Xiawei Cheng
Processes 2025, 13(9), 2986; https://doi.org/10.3390/pr13092986 - 18 Sep 2025
Viewed by 325
Abstract
Distribution grids are essential to power systems, as they ensure the safe and reliable operation of power systems. It is important to forecast the operational status of each distribution transformer. Consequently, there is an urgent need to develop load forecasting models that exhibit [...] Read more.
Distribution grids are essential to power systems, as they ensure the safe and reliable operation of power systems. It is important to forecast the operational status of each distribution transformer. Consequently, there is an urgent need to develop load forecasting models that exhibit robust generalization and exceptional accuracy. However, distribution transformers are numerous, exhibit strong inherent features. Single-device load samples are scarce, with widespread few-shot issues. Traditional models rely on massive amounts of data and computational resources, making it challenging to balance computational costs, generalization ability, and accuracy. To address these issues, this paper analyzes the inherent features of distribution transformers and fine-tunes large language models using parameter-efficient fine-tuning methods to reduce training costs while preserving their generalization capabilities and accuracy. Experimental results demonstrate that this method achieves significantly higher accuracy than baseline models in few-shot scenarios. Full article
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23 pages, 2365 KB  
Article
What Is the Process? A Metamodel of the Requirements Elicitation Process Derived from a Systematic Literature Review
by Mauricio Hidalgo, Fernando Yanine, Rodrigo Paredes, Jonathan Frez and Mauricio Solar
Processes 2025, 13(1), 20; https://doi.org/10.3390/pr13010020 - 25 Dec 2024
Cited by 2 | Viewed by 2239
Abstract
Requirements elicitation is a fundamental process in software engineering, essential for aligning software products with user needs and project objectives. As software projects become more complex, effective elicitation methods are vital for capturing accurate and comprehensive requirements. Despite the variety of available elicitation [...] Read more.
Requirements elicitation is a fundamental process in software engineering, essential for aligning software products with user needs and project objectives. As software projects become more complex, effective elicitation methods are vital for capturing accurate and comprehensive requirements. Despite the variety of available elicitation methods, practitioners face persistent challenges such as capturing tacit knowledge, managing diverse stakeholder needs, and addressing ambiguities in requirements. Moreover, although elicitation is recognized as a core process for gathering and analyzing system objectives, there is a lack of a unified and systematic framework to guide practitioners—especially newcomers—through the activity. To address these challenges, we provide a comprehensive analysis of existing elicitation methods, aiming to contribute to better alignment between software products and project objectives, ultimately improving software engineering practices. We do so by performing a systematic literature review identifying crosscutting steps, common techniques, tools, and approaches that define the core activities of the elicitation process. We synthesize our findings into a metamodel that structures software elicitation processes. This review uncovers various elicitation methods—such as collaborative workshops, interviews, and prototyping—each demonstrating unique strengths in different project contexts. It also highlights significant limitations, including stakeholder misalignment and incomplete requirements capture, which continue to reduce the effectiveness of elicitation processes. Finally, our study seeks to contribute to understanding requirements elicitation methods by providing a comprehensive view of their current strengths and limitations through a metamodel enabling the structuring and optimization of elicitation processes. Full article
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