sustainability-logo

Journal Browser

Journal Browser

Climate Change and Sustainability: Intelligent Operation and Maintenance of New Energy Systems

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 644

Special Issue Editors


E-Mail Website
Guest Editor
School of Renewable Energy, North China Electric Power University, Beijing 102206, China
Interests: concentrated solar thermal power; forecast of solar plant; daylighting
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
National Engineering Laboratory for Biomass Power Generation Equipment, School of Renewable Energy, North China Electric Power University, Beijing 102206, China
Interests: sustainability; renewable energy; waste to energy; surface engineering materials; energy equipment

Special Issue Information

Dear Colleagues,

With the advent of an era of large-scale penetration of new energy, the intelligent operation and maintenance of new energy systems, including solar, wind, biomass, and energy storage systems, is crucial and can result in significant benefits. This Special Issue aims to explore the methods and technologies underpinning the intelligent operation and maintenance of new energy systems, including performance evaluation, fault diagnosis, accident alarms, life extension, and recycling, so as to improve energy utilization and the revenue generated by power plants.

Intelligent operation and maintenance is the key to realizing the unattended or less manpower-intensive operation and maintenance and the long life of new energy systems, and is crucial for a new generation of power systems dominated by new energy. We believe that through cooperation and sharing we can jointly address the challenges of the new energy sector and provide more sustainable energy solutions on a global scale. Therefore, we are launching this Special Issue to bring together the wisdom and experience of scholars from across various fields to explore the cutting-edge research results in the field of intelligent operation and maintenance of new energy systems, and contribute to the green and low-carbon development of the energy sector.

Prof. Dr. Jifeng Song
Prof. Dr. Zuopeng Qu
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. 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

  • new energy systems
  • solar energy
  • intelligent operation and maintenance
  • forecasting and prediction technology
  • energy economics
  • data analysis
  • energy storage systems
  • integrated energy systems
  • energy policy
  • fault diagnosis
  • biomass

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 23239 KiB  
Article
Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer
by Xilong Lin, Yisen Niu, Zixuan Yan, Lianglin Zou, Ping Tang and Jifeng Song
Sustainability 2024, 16(14), 6102; https://doi.org/10.3390/su16146102 - 17 Jul 2024
Viewed by 471
Abstract
Accurate forecasting of PV power not only enhances the utilization of solar energy but also assists power system operators in planning and executing efficient power management. The Temporal Convolutional Network (TCN) is utilized for feature extraction from the data, while the White Shark [...] Read more.
Accurate forecasting of PV power not only enhances the utilization of solar energy but also assists power system operators in planning and executing efficient power management. The Temporal Convolutional Network (TCN) is utilized for feature extraction from the data, while the White Shark Optimization (WSO) algorithm optimizes the TCN parameters. Given the extensive dataset and the complex variables influencing PV output in this study, the maximal information coefficient (MIC) method is employed. Initially, mutual information values are computed for the base data, and less significant variables are eliminated. Subsequently, the refined data are fed into the TCN, which is fine-tuned using WSO. Finally, the model outputs the prediction results. For testing, one year of data from a dual-axis tracking PV system is used, and the robustness of the model is further confirmed using data from single-axis and stationary PV systems. The findings demonstrate that the MIC-WSO-TCN model outperforms several benchmark models in terms of accuracy and reliability for predicting PV power. Full article
Show Figures

Figure 1

Back to TopTop