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Applied Sciences
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Emerging Trends in Energy Management: Techniques, Applications and Future Directions
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Emerging Trends in Energy Management: Techniques, Applications and Future Directions

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 1456

Special Issue Editors

Dr. Roberto Romano

E-Mail Website
Guest Editor
1. Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00044 Frascati, Italy
2. DTT S. c. a r. l., 00044 Frascati, Italy
Interests: power distribution system operation and planning; smart grids; demand response; renewable energy; energy storage systems; smart house; energy management systems
Dr. Alessandro Lampasi

E-Mail Website
Guest Editor
1. Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00044 Frascati, Italy
2. DTT S. c. a r. l., 00044 Frascati, Italy
Interests: supercapacitors; power supplies and electrical systems; characterization, modeling and simulation of supercapacitors; hybrid energy storage
Special Issues, Collections and Topics in MDPI journals
Dr. Sabino Pipolo

E-Mail Website
Guest Editor
1. Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00044 Frascati, Italy
2. DTT S. c. a r. l., 00044 Frascati, Italy
Interests: electrical and electronic systems for nuclear fusion; advanced power electronic systems for smart grid applications; energy storage; solid-state transformers, design and control of power electronics converters; design and control of matrix converters

Special Issue Information

Dear Colleagues,

The interest in efficient and sustainable energy management has gained unprecedented urgency due to rising global energy demands and environmental concerns. The field is undergoing a significant transformation driven by emerging technologies. Advances in smart home technologies, such as sensors, automation, and real-time data analytics, are enabling homes to optimize energy use, enhance comfort, and integrate renewable resources like solar panels more effectively. Simultaneously, smart grids are enhancing communication between energy providers and consumers, improving grid stability, and facilitating the integration of renewable energy. Technologies like artificial intelligence (AI) and machine learning are providing advanced tools for predictive analytics and dynamic energy management. Additionally, advancements in energy storage are boosting the reliability and scalability of renewable sources, while digital innovations are enhancing the security and efficiency of energy transactions.

This Special Issue invites researchers to submit contributions that explore these advancements, focusing on techniques, applications, and future directions in energy management, including but not limited to these aspects. The aim is to gather high-quality scientific papers that offer insights into how these technologies are transforming the field and to identify opportunities for further research and innovation.

Dr. Roberto Romano
Dr. Alessandro Lampasi
Dr. Sabino Pipolo
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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

  • smart grids
  • smart home technologies
  • artificial intelligence
  • renewable energy sources
  • energy storage

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Published Papers (2 papers)

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18 pages, 3717 KiB  
Article
Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning
by Ferdi Doğan, Saadin Oyucu, Derya Betul Unsal, Ahmet Aksöz and Majid Vafaeipour
Appl. Sci. 2025, 15(1), 336; https://doi.org/10.3390/app15010336 - 1 Jan 2025
Viewed by 930
Abstract
The real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and [...] Read more.
The real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study’s primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sources Full article
(This article belongs to the Special Issue Emerging Trends in Energy Management: Techniques, Applications and Future Directions)
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Other

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42 pages, 1491 KiB  
Systematic Review
Systematic Review of Hierarchical and Multi-Agent Optimization Strategies for P2P Energy Management and Electric Machines in Microgrids
by Paul Arévalo, Danny Ochoa-Correa, Edisson Villa-Ávila, Vinicio Iñiguez-Morán and Patricio Astudillo-Salinas
Appl. Sci. 2025, 15(9), 4817; https://doi.org/10.3390/app15094817 - 26 Apr 2025
Viewed by 180
Abstract
The growing complexity of distributed energy systems and the rise of peer-to-peer energy markets demand innovative solutions for efficient, resilient, and sustainable energy management. However, existing research often remains fragmented, with limited integration between control strategies, optimization frameworks, and practical implementation. This paper [...] Read more.
The growing complexity of distributed energy systems and the rise of peer-to-peer energy markets demand innovative solutions for efficient, resilient, and sustainable energy management. However, existing research often remains fragmented, with limited integration between control strategies, optimization frameworks, and practical implementation. This paper presents a comprehensive systematic review, following the PRISMA methodology, that synthesizes findings from 94 high-quality studies and addresses the lack of consolidated insights across technical, operational, and architectural layers. This review highlights advancements in six key areas: optimization and modeling, multi-agent systems, simulations, blockchain and smart contracts, robust frameworks, and electric machines. Despite progress, several studies reveal challenges related to scalability, data privacy, computational complexity, and system adaptability, particularly in dynamic and decentralized environments. Stochastic–robust optimization and multi-agent systems improve decentralized coordination, while blockchain enhances security and automation in peer-to-peer trading. Simulations validate energy strategies, bridging theory and practice, and electric machines support renewable integration and grid flexibility. The synthesis underscores the need for unified frameworks that combine artificial intelligence, predictive control, and secure communication protocols. This review aims to provide a roadmap for advancing distributed energy systems toward scalable, resilient, and sustainable energy solutions. Full article
(This article belongs to the Special Issue Emerging Trends in Energy Management: Techniques, Applications and Future Directions)
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