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: 25 September 2025 | Viewed by 416
Special Issue Editors
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
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
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