Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.7
3.3
Journals
Sustainability
Special Issues
Energy Conservation Towards a Low-Carbon and Sustainability Future
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Energy Conservation Towards a Low-Carbon and Sustainability Future

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

Deadline for manuscript submissions: closed (25 September 2025) | Viewed by 852

Special Issue Editors

Prof. Dr. Yongming Han

E-Mail Website1 Website2
Guest Editor
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Interests: early warning modeling; safety analysis; efficiency evaluation and prediction; data fusion and fuzzy hierarchy fusion; software design and development; carbon emission reduction and system optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Xuan Hu

E-Mail Website
Guest Editor
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Interests: Interests: time series prediction; soft sensor modeling; early warning modeling; software design and development; energy efficiency optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traditional energy sources such as oil and coal are limited resources, and unsustainable exploitation will lead to the risk of depletion. Saving energy and moving towards a low-carbon and sustainable future are increasingly attracting attention from the environmental and energy research community, especially in the fields of energy efficiency, renewable energy, the built environment, and industrial processes. Traditional energy management and evaluation methods have disadvantages such as difficulty in efficiency evaluation, high complexity, and high cost. Through data analysis and artificial intelligence technologies, such as neural networks, machine learning, time series analysis, and big data technology, energy efficiency is evaluated based on a data-driven approach. This can reduce unnecessary influencing factors in the actual process of energy efficiency evaluation, quickly establish energy savings and energy conservation towards a low-carbon and sustainability future model, and help achieve energy conservation and carbon emission reductions, thereby improving the environmental efficiency of energy utilization. 

In this Special Issue, original research articles and reviews are welcome and research areas may include, but are not limited to:  

  • Industrial process optimization;
  • Energy efficiency improvements;
  • Renewable energy technology;
  • Smart grids and energy storage;
  • The integration and optimization of renewable energy systems;
  • Carbon capture and storage technology;
  • High-performance mechanical system design;
  • Energy data analysis;
  • Automation and control systems;
  • New energy materials. 

We look forward to receiving your contributions. 

You may choose our Joint Special Issue in Big Data and Cognitive Computing. 

Prof. Dr. Yongming Han
Dr. Xuan Hu
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

  • data-driven
  • intelligent optimization
  • efficiency evaluation
  • artificial intelligence
  • data mining and fusion
  • environmental protection
  • energy saving and optimization
  • energy consumption forecast
  • air pollution emissions
  • renewable energy conversion and utilization
  • hydrogen energy
  • high-efficiency energy devices
  • neural networks
  • deep learning
  • environmental efficiency

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 2230 KB  
Article
A Load Forecasting Model Based on Spatiotemporal Partitioning and Cross-Regional Attention Collaboration
by Xun Dou, Ruiang Yang, Zhenlan Dou, Chunyan Zhang, Chen Xu and Jiacheng Li
Sustainability 2025, 17(18), 8162; https://doi.org/10.3390/su17188162 - 10 Sep 2025
Viewed by 277
Abstract
With the advancement of new power system construction, thermostatically controlled loads represented by regional air conditioning systems are being extensively integrated into the grid, leading to a surge in the number of user nodes. This large-scale integration of new loads creates challenges for [...] Read more.
With the advancement of new power system construction, thermostatically controlled loads represented by regional air conditioning systems are being extensively integrated into the grid, leading to a surge in the number of user nodes. This large-scale integration of new loads creates challenges for the grid, as the resulting load data exhibits strong periodicity and randomness over time. These characteristics are influenced by factors like temperature and user behavior. At the same time, spatially adjacent nodes show similarities and clustering in electricity usage. This creates complex spatiotemporal coupling features. These complex spatiotemporal characteristics challenge traditional forecasting methods. Their high model complexity and numerous parameters often lead to overfitting or the curse of dimensionality, which hinders both prediction accuracy and efficiency. To address this issue, this paper proposes a load forecasting method based on spatiotemporal partitioning and collaborative cross-regional attention. First, a spatiotemporal similarity matrix is constructed using the Shape Dynamic Time Warping (ShapeDTW) algorithm and an adaptive Gaussian kernel function based on the Haversine distance. Spectral clustering combined with the Gap Statistic criterion is then applied to adaptively determine the optimal number of partitions, dividing all load nodes in the power grid into several sub-regions with homogeneous spatiotemporal characteristics. Second, for each sub-region, a local Spatiotemporal Graph Convolutional Network (STGCN) model is built. By integrating gated temporal convolution with spatial feature extraction, the model accurately captures the spatiotemporal evolution patterns within each sub-region. On this basis, a cross-regional attention mechanism is designed to dynamically learn the correlation weights among sub-regions, enabling collaborative fusion of global features. Finally, the proposed method is evaluated on a multi-node load dataset. The effectiveness of the approach is validated through comparative experiments and ablation studies (that is, by removing key components of the model to evaluate their contribution to the overall performance). Experimental results demonstrate that the proposed method achieves excellent performance in short-term load forecasting tasks across multiple nodes. Full article
(This article belongs to the Special Issue Energy Conservation Towards a Low-Carbon and Sustainability Future)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop