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Energy Efficiency and Innovative Material Application in Sustainable Buildings

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 2987

Special Issue Editors


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Guest Editor
Mechanics of Continuous Media and Theory of Structures Department, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: multi-objective optimization; structure optimization; lifecycle assessment; social sustainability of infrastructures; metamodels

Special Issue Information

Dear Colleagues:

The Special Issue “Energy Efficiency and Innovative Material Application in Sustainable Buildings” focuses on advancing energy-efficient practices and novel materials in construction, crucial for global sustainability. Buildings account for significant energy use and carbon emissions, necessitating innovations to enhance efficiency and to reduce environmental impact. This Special Issue aims to facilitate interdisciplinary dialogue and to highlight cutting-edge research in sustainable architecture and engineering. Aligned with the journal's scope, it seeks to inspire professionals while promoting sustainable design and construction excellence. Key themes include energy-efficient design, innovative materials, intelligent building technologies, lifecycle assessment, and case studies illustrating best practices. Through these avenues, this Special Issue aims to contribute to a more sustainable and resilient built environment, addressing critical challenges and fostering progress towards a greener future.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Energy-Efficient Building Design and Retrofitting;
  • Nanotechnology Applications for Energy-Efficient Building Materials;
  • Integration of Renewable Energy Systems in Urban Buildings;
  • Sustainable Concrete Solutions for Green Construction;
  • Emerging Trends in Energy-Efficient HVAC Systems;
  • Smart Building Systems and Technologies ;
  • Circular Economy Approaches in Building Material Management;
  • The Role of Artificial Intelligence in Optimizing Building Energy Performance;
  • Innovations in Daylighting and Natural Ventilation Strategies;
  • Net-Zero Energy Building Case Studies: Lessons Learned and Future Directions;
  • Case Studies and Best Practices;
  • Regenerative Design in Architecture and Construction.

We look forward to receiving your contributions.

Prof. Dr. Víctor Yepes
Dr. Lorena Yepes-Bellver
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

  • energy efficiency
  • innovative materials
  • sustainable buildings
  • smart buildings
  • green construction
  • circular economy

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Published Papers (2 papers)

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Research

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14 pages, 4898 KiB  
Article
Artificial Neural Network and Kriging Surrogate Model for Embodied Energy Optimization of Prestressed Slab Bridges
by Lorena Yepes-Bellver, Alejandro Brun-Izquierdo, Julián Alcalá and Víctor Yepes
Sustainability 2024, 16(19), 8450; https://doi.org/10.3390/su16198450 - 28 Sep 2024
Viewed by 900
Abstract
The main objective of this study is to assess and contrast the efficacy of distinct spatial prediction methods in a simulation aimed at optimizing the embodied energy during the construction of prestressed slab bridge decks. A literature review and cross-sectional analysis have identified [...] Read more.
The main objective of this study is to assess and contrast the efficacy of distinct spatial prediction methods in a simulation aimed at optimizing the embodied energy during the construction of prestressed slab bridge decks. A literature review and cross-sectional analysis have identified crucial design parameters that directly affect the design and construction of bridge decks. This analysis determines the critical design variables to improve the deck’s energy efficiency, providing practical guidance for engineers and professionals in the field. The methods analyzed in this study are ordinary Kriging and a multilayer perceptron neural network. The methodology involves analyzing the predictive performance of both models through error analysis and assessing their ability to identify local optima on the response surface. The results show that both models generally overestimate the observed values. The Kriging model with second-order polynomials yields a 4% relative error at the local optimum, while the neural network achieves lower root mean square errors (RMSEs). Neither the Kriging model nor the neural network provides precise predictions but point to promising solution regions. Optimizing the response surface to find a local minimum is crucial. High slenderness ratios (around 1/28) and 40 MPa concrete grade are recommended to improve energy efficiency. Full article
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Review

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29 pages, 5865 KiB  
Review
Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems
by Kawakib Arar Tahir, Javier Ordóñez and Juanjo Nieto
Sustainability 2024, 16(12), 5156; https://doi.org/10.3390/su16125156 - 17 Jun 2024
Cited by 2 | Viewed by 1156
Abstract
Hybrid energy systems (HESs) integrate renewable sources, storage, and optionally conventional energies, offering a sustainable alternative to fossil fuels. Microgrids (MGs) bolster this integration, enhancing energy management, resilience, and reliability across different levels. This study, emphasizing the need for refined optimization methods, investigates [...] Read more.
Hybrid energy systems (HESs) integrate renewable sources, storage, and optionally conventional energies, offering a sustainable alternative to fossil fuels. Microgrids (MGs) bolster this integration, enhancing energy management, resilience, and reliability across different levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010 to 2023, analyzed using SciMAT v1.1.04 software. It segments the research into two periods, 2010–2019 and 2020–2023, revealing a surge in MOO focus, particularly in the latter period, with a 35% increase in MOO-related research. This indicates a shift toward comprehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of artificial intelligence (AI) in optimizing HMGS within the MOO framework signals a move toward more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research. Full article
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