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Energies
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Studies on Clean and Sustainable Energy Utilization
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Studies on Clean and Sustainable Energy Utilization

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 2439

Special Issue Editors

Dr. Xiaoqing Lin

E-Mail Website
Guest Editor
Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
Interests: waste-to-energy; clean and sustainable energy utilization; energy engineering; waste management
Special Issues, Collections and Topics in MDPI journals
Dr. Zongliang Zuo

E-Mail Website
Guest Editor
School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China
Interests: efficient energy conversion and low-carbon clean utilization; solid waste energy; valuable metals recovery
Special Issues, Collections and Topics in MDPI journals
Dr. Chen Sun

E-Mail Website
Guest Editor
1. Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
2. Institute of Zhejiang University, Quzhou 32400, China
Interests: waste to carbon-based functional materials; advanced water purification technologies; safe disposal of hazardous wastes

Special Issue Information

Dear Colleagues,

Pursuing clean and sustainable energy solutions has been critical in the face of growing energy demands and increasing environmental concerns. This Special Issue seeks to gather ground-breaking research and practical advancements that push the boundaries of generating, using, and using sustainable energy.

We are particularly interested in contributions that address the following areas:

  • Innovative Waste Incineration Technologies: Exploring new methods to make waste incineration cleaner and more efficient, including advancements in energy recovery and pollution control.
  • Dioxin Emission Control: Investigating the formation and suppression of dioxins in incineration processes and the latest technologies for their adsorption, filtration, and catalytic degradation.
  • Safe and Sustainable Fly Ash Management: Approaches to minimize the environmental impact of fly ash, including methods for its safe disposal and potential resource recovery.
  • Smart Systems for Waste Resource Utilization: Development of intelligent systems and databases to optimize the resource utilization of solid wastes, aiming for higher efficiency and sustainability.
  • CO2 Capture, Utilization, and Storage: Cutting-edge research on technologies for capturing, utilizing, and storing CO2, focusing on reducing greenhouse gas emissions and integrating these processes into existing energy systems.
  • Resource Recovery from Waste: Innovative strategies for converting waste materials into valuable resources, supporting the move towards a circular economy.

This Special Issue aims to serve as a forum for sharing innovative research contributing to the global effort to transition to a cleaner, more sustainable energy future. We welcome submissions offering new perspectives, presenting experimental results, or introducing technological innovations.

We look forward to your contributions to this Special Issue.

Dr. Xiaoqing Lin
Dr. Zongliang Zuo
Dr. Chen Sun
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. 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

  • waste-to-energy (WtE)
  • pollution control
  • smart system for energy utilization
  • resource recovery from waste
  • sustainable and clean energy
  • CO2 capture, utilization, and storage

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Review

17 pages, 389 KiB  
Review
A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges
by Zongxu Liu, Hui Guo, Yingshuai Zhang and Zongliang Zuo
Energies 2025, 18(2), 350; https://doi.org/10.3390/en18020350 - 15 Jan 2025
Cited by 1 | Viewed by 2196
Abstract
Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over [...] Read more.
Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over traditional physical and statistical models. Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. The review also explores critical aspects such as data preprocessing, feature selection strategies, and model optimization techniques, which significantly enhance prediction accuracy and robustness. Challenges such as data acquisition difficulties, complex terrain influences, and sensor quality issues are examined in depth, with proposed solutions discussed. Additionally, the paper highlights future research directions, including the potential of multi-model fusion, emerging deep learning technologies like Transformers, and the integration of smart sensors and IoT technologies to develop intelligent, automated, and reliable prediction systems. By addressing existing challenges and leveraging advanced machine learning techniques, this work provides valuable insights into the current state of wind power prediction research and offers strategic guidance for enhancing the applicability and reliability of prediction models in practical scenarios. Full article
(This article belongs to the Special Issue Studies on Clean and Sustainable Energy Utilization)
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