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Innovative and Smart Renewable Energy Technologies for Developing Low-Carbon and Sustainable Societies

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 785

Special Issue Editor

Dr. Alireza Dehghani-Sanij

E-Mail Website
Guest Editor
Waterloo Institute for Sustainable Energy (WISE), University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: clean and renewable energy; energy production, conversion and storage; energy management and optimization; smart, hybrid energy systems; sustainable development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Innovative, smart renewable energy technologies are crucial in developing low-carbon and sustainable societies. These technologies integrate advanced solutions such as artificial intelligence, smart grids, and energy storage and conversion systems to optimize energy production and consumption. By harnessing renewable sources more efficiently, they reduce greenhouse gas emissions and dependence on carbon-based fuels. Smart energy management systems enhance supply stability and reliability while minimizing waste. Sustainable urban planning, incorporating renewable energy solutions, promotes eco-friendly infrastructure and smart cities. Governments and industries worldwide are investing in these advancements to achieve sustainability and decarbonization goals while meeting growing energy demands. Research and policy support are essential for accelerating the adoption of these technologies. Public awareness and education further drive the transition toward cleaner energy solutions. Finally, these innovations pave the way for a more resilient and environmentally responsible future.

This Special Issue focuses on addressing critical knowledge gaps related to innovative, smart renewable energy technologies for the development of low-carbon and sustainable societies. It aims at showcasing cutting-edge research that advances the adoption of these technologies, promoting environmental quality and resilience in societies.

Potential topics of interest include, but are not limited to, the following:

  • Energy sustainability, resilience, and climate adaptability in societies;
  • Decarbonization strategies for buildings/societies through innovative technologies;
  • Smart, hybrid energy systems;
  • Energy-efficient and net-zero energy buildings;
  • Renewable, affordable, and reliable energy technologies;
  • Sustainable development and climate change mitigation goals in societies;
  • Heating, ventilation, and air-conditioning (HVAC) and energy recovery systems;
  • Advanced energy storage and conversion technologies;
  • Sustainable development and green architecture.

Original research articles, review articles, case studies, and technical notes are welcome in this Special Issue.

Dr. Alireza Dehghani-Sanij
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

  • clean and renewable energy
  • energy sources and production
  • energy conversion and storage
  • energy efficiency
  • energy conservation and recovery
  • energy management and optimization
  • smart, hybrid energy systems
  • energy equity, security, and access
  • energy and buildings
  • climate change and global warming
  • sustainable societies
  • sustainability and decarbonization

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

Published Papers (2 papers)

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Research

25 pages, 3437 KiB  
Article
An Innovative Framework for Forecasting the State of Health of Lithium-Ion Batteries Based on an Improved Signal Decomposition Method
by Ting Zhu, Wenbo Wang, Yu Cao, Xia Liu, Zhongyuan Lai and Hui Lan
Sustainability 2025, 17(11), 4847; https://doi.org/10.3390/su17114847 - 25 May 2025
Viewed by 140
Abstract
The declining trend of battery aging has strong nonlinearity and volatility, which poses great challenges to the prediction of battery’s state of health (SOH). In this research, an innovative framework is initially put forward for SOH prediction. First, partial incremental capacity analysis (PICA) [...] Read more.
The declining trend of battery aging has strong nonlinearity and volatility, which poses great challenges to the prediction of battery’s state of health (SOH). In this research, an innovative framework is initially put forward for SOH prediction. First, partial incremental capacity analysis (PICA) is carried out to analyze the performance degradation within a specific voltage range. Subsequently, the height of the peak, the position of the peak, and the area beneath the peak of the IC curves are retrieved and used as health features (HFs). Moreover, improved ensemble empirical mode decomposition based on fractal dimension (FEEMD) is first proposed and utilized to decompose HFs to reduce the nonlinearity and fluctuations. Additionally, a bidirectional gated recurrent unit with an attention mechanism (BiGRU-AM) is constructed for the prognosis of these sub-layers. Finally, the effectiveness and robustness of the proposed prognosis framework are validated using two battery datasets. The results of three groups of comparative experiments demonstrate that the maximum root mean squared error (RMSE) and mean absolute error (MAE) values reach merely 0.55% and 0.59%, respectively. This further demonstrates that the proposed FEEMD outperforms other benchmark models and can offer a reliable foundation for the health prognosis of lithium-ion batteries. Full article
(This article belongs to the Special Issue Innovative and Smart Renewable Energy Technologies for Developing Low-Carbon and Sustainable Societies)
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23 pages, 4887 KiB  
Article
Occupancy-Based Predictive AI-Driven Ventilation Control for Energy Savings in Office Buildings
by Violeta Motuzienė, Jonas Bielskus, Rasa Džiugaitė-Tumėnienė and Vidas Raudonis
Sustainability 2025, 17(9), 4140; https://doi.org/10.3390/su17094140 - 3 May 2025
Viewed by 357
Abstract
Despite stricter global energy codes, performance standards, and advanced renewable technologies, the building sector must accelerate its transition to zero carbon emissions. Many studies show that new buildings, especially non-residential ones, often fail to meet projected performance levels due to poor maintenance and [...] Read more.
Despite stricter global energy codes, performance standards, and advanced renewable technologies, the building sector must accelerate its transition to zero carbon emissions. Many studies show that new buildings, especially non-residential ones, often fail to meet projected performance levels due to poor maintenance and management of HVAC systems. The application of predictive AI models offers a cost-effective solution to enhance the efficiency and sustainability of these systems, thereby contributing to more sustainable building operations. The study aims to enhance the control of a variable air volume (VAV) system using machine learning algorithms. A novel ventilation control model, AI-VAV, is developed using a hybrid extreme learning machine (ELM) algorithm combined with simulated annealing (SA) optimisation. The model is trained on long-term monitoring data from three office buildings, enhancing robustness and avoiding the data reliability issues seen in similar models. Sensitivity analysis reveals that accurate occupancy prediction is achieved with 8500 to 10,000 measurement steps, resulting in potential additional energy savings of up to 7.5% for the ventilation system compared to traditional VAV systems, while maintaining CO2 concentrations below 1000 ppm, and up to 12.5% if CO2 concentrations are slightly above 1000 ppm for 1.5% of the time. Full article
(This article belongs to the Special Issue Innovative and Smart Renewable Energy Technologies for Developing Low-Carbon and Sustainable Societies)
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