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Future Directions in Energy Transition and Sustainable Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 1232

Special Issue Editor


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Guest Editor
Department of Research and Innovation, Research and Education Promotion Association (REPA), 1401 21st St. #6172, Sacramento, CA 95811, USA
Interests: artificial intelligence; machine learning applications in energy systems optimization of energy systems; power system analysis; environmental mitigation strategies within interconnected grid frameworks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of energy systems, driven by technological advancements and increasing environmental concerns, demands innovative approaches to manage and optimize these systems sustainably. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools, revolutionizing how energy systems are analyzed, optimized, and managed. This Special Issue, titled "Future Directions in Energy Transition and Sustainable Management", aims to explore the intersection of AI/ML applications, optimization strategies, and environmental mitigation methods within interconnected grid frameworks. This research area is critical for achieving sustainable energy goals, reducing environmental impact, and enhancing the efficiency and reliability of power systems.

The primary aim of this Special Issue is to gather cutting-edge research and comprehensive reviews that address the challenges and opportunities in energy transition and sustainable management. By focusing on AI and ML applications, optimization techniques, and environmental strategies, this issue seeks to contribute to the body of knowledge that supports the development of resilient and sustainable energy systems. Its scope aligns with the journal's mission to advance scientific understanding and practical solutions in the field of energy systems and sustainability.

This Special Issue invites original research articles and review papers that delve into, but are not limited to, the following themes:

  • The development of AI/ML algorithms for energy forecasting, load prediction, and demand-side management;
  • The use of AI/ML in renewable energy integration and smart grid technologies;
  • Case studies demonstrating AI/ML applications in enhancing energy system reliability and efficiency;
  • Novel optimization algorithms for energy distribution, storage, and generation;
  • Techniques for optimizing energy consumption in industrial, commercial, and residential sectors;
  • Advanced methods for power system stability, reliability, and resilience analysis;
  • Strategies for reducing greenhouse gas emissions and other pollutants in power generation and distribution;
  • The implementation of sustainable practices in grid management to minimize environmental impact;
  • Evaluations of policies and regulatory frameworks supporting environmental sustainability in energy systems.

I look forward to receiving your contributions.

Dr. Danish Mir Sayed Shah
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 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. Sustainability 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

  • artificial intelligence in energy
  • machine learning for energy systems
  • energy optimization
  • power system analysis
  • renewable energy integration
  • sustainable energy management
  • environmental mitigation in grids

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Published Papers (1 paper)

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Research

21 pages, 6203 KiB  
Article
Short-Term Residential Load Forecasting Based on the Fusion of Customer Load Uncertainty Feature Extraction and Meteorological Factors
by Wenzhi Cao, Houdun Liu, Xiangzhi Zhang, Yangyan Zeng and Xiao Ling
Sustainability 2025, 17(3), 1033; https://doi.org/10.3390/su17031033 - 27 Jan 2025
Cited by 2 | Viewed by 846
Abstract
With the proliferation of distributed energy resources, advanced metering infrastructure, and advanced communication technologies, the grid is transforming into a flexible, intelligent, and collaborative system. Short-term electric load forecasting for individual residential customers is playing an increasingly important role in the operation and [...] Read more.
With the proliferation of distributed energy resources, advanced metering infrastructure, and advanced communication technologies, the grid is transforming into a flexible, intelligent, and collaborative system. Short-term electric load forecasting for individual residential customers is playing an increasingly important role in the operation and planning of the future grid. Predicting the electrical load of individual households is more challenging with higher uncertainty and volatility at the household level compared to the total electrical load at the feeder and regional levels. The previous research results show that the accuracy of forecasting using machine learning and a single deep learning model is far from adequate and there is still room for improvement. Full article
(This article belongs to the Special Issue Future Directions in Energy Transition and Sustainable Management)
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