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Advanced Design and Control Solutions for Grid-Interactive Energy Efficient Buildings

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

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 2212

Special Issue Editors


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Guest Editor
1. Director, Sustainable Energy Research Center (SERC), Sultan Qaboos University, PC123, Alkhoud P.O. Box 54, Oman
2. Department of Civil and Architectural Engineering, Sultan Qaboos University, PC 123, Alkhoud P.O. Box 33, Oman
Interests: net-zero energy buildings; renewable and sustainable energy applications; smart buildings and cities; design and analysis of building energy systems; buildings energy efficiency; thermal analysis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Sultan Qaboos University, PC 123, Alkhoud P.O. Box 33, Oman
Interests: renewable energy systems design and integration; microgrids; smart grids; machine learning; optimization; distributed resources planning; power electronics interface and control for renewable energy systems; electric vehicles; energy storage; green hydrogen production
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, Mechatronics Engineering Program Coordinator, Sultan Qaboos University, PC 123, Alkhoud P.O. Box 123, Oman
Interests: robotics; system dynamics and control; mechatronics systems design; instrumentation and measurement; in-pipe inspection robot; biologically inspired control

Special Issue Information

Dear Colleagues,

The integration of advanced design and control techniques for grid-interactive buildings has become an imperative element in the construction of energy efficient buildings. Grid-interactive buildings are commonly characterized by their high energy efficiency, integration with renewable energy sources, and ability to interact with the electricity grid. This Special Issue will focus on "Advanced Design and Control Solutions for Grid-Interactive Energy Efficient Buildings" with the aim of exploring state-of-the-art approaches and cutting-edge research on grid-interactive buildings. Topics of interest include, but are not limited to:

  1. Smart building technologies for energy optimization;
  2. Building-integrated renewable energy;
  3. Demand response strategies and load management in smart cities;
  4. Advanced building automation and control systems;
  5. Machine learning and artificial intelligence applications in building control;
  6. Grid-interactive building modeling and simulation;
  7. Building-to-grid (B2G) and vehicle-to-grid (V2G) integration;
  8. Microgrids and distributed energy resource (DER) management;
  9. Economic and market analysis of grid-interactive energy efficient buildings;
  10. Model predictive control (MPC) for optimizing the energy consumption and grid interaction of buildings;
  11. Demand response strategies for responding to real-time grid signals;
  12. Real-world implementation and case studies of grid-interactive energy efficient buildings.

Dr. Saleh N. J. Al-Saadi
Dr. Razzaqul Ahshan
Dr. Riadh Zaier
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

  • building-to-grid (B2G) integration
  • vehicle-to-grid (V2G) integration
  • smart buildings
  • grid-interaction buildings
  • building automation and control
  • machine learning
  • artificial intelligence
  • building-integrated renewable energy
  • smart microgrids

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Published Papers (2 papers)

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Research

35 pages, 17456 KiB  
Article
Innovative Energy Efficiency in HVAC Systems with an Integrated Machine Learning and Model Predictive Control Technique: A Prospective Toward Sustainable Buildings
by Khaled Almazam, Omar Humaidan, Nahla M. Shannan, Faizah Mohammed Bashir, Taha Gammoudi and Yakubu Aminu Dodo
Sustainability 2025, 17(7), 2916; https://doi.org/10.3390/su17072916 - 25 Mar 2025
Viewed by 549
Abstract
This study introduces a novel approach, combining radial basis function neural network (RBFNN) and model predictive control (MPC) techniques to enhance energy efficiency in HVAC systems for sustainable buildings. The proposed methodology is evaluated in a single-story commercial and residential building in Najran, [...] Read more.
This study introduces a novel approach, combining radial basis function neural network (RBFNN) and model predictive control (MPC) techniques to enhance energy efficiency in HVAC systems for sustainable buildings. The proposed methodology is evaluated in a single-story commercial and residential building in Najran, Saudi Arabia, utilizing new input parameters such as ambient temperature, cooling load, and compressor speed, alongside output metrics including room temperature and total exergy destruction and coefficient of performance (CoP) of the HVAC system. Significant improvements in energy management practices were observed, with a reduction in energy consumption by approximately 15% compared to conventional control models. The model’s predictive capabilities were validated against real-world electricity consumption data, demonstrating a high correlation with discrepancies ranging from 0.2% to 2.5%. Furthermore, the integration of machine learning techniques enabled more precise control of HVAC operations, addressing concerns regarding the system’s dynamic behavior and optimizing performance under varying occupancy patterns. While in the commercial building, the model achieves RMSE and CV values of approximately 1.0 and 0.61 for room temperature, 1.21 and 0.48 for exergy destruction, and 0.65 and 0.30 for CoP. However, for the residential building, RMSE and CV values are approximately 0.95 and 0.69 for room temperature, 1.08 and 0.31 for exergy destruction, and 0.55 and 0.27 for CoP. Full article
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17 pages, 602 KiB  
Article
Green Order Sorting Problem in Cold Storage Solved by Genetic Algorithm
by Furkan Yener and Harun Resit Yazgan
Sustainability 2024, 16(20), 9062; https://doi.org/10.3390/su16209062 - 19 Oct 2024
Cited by 2 | Viewed by 1289
Abstract
This study investigates the efficiency of cold storage warehouses and contributes to sustainable supply chain management by integrating eco-friendly practices into storage operations. In facilities for milk and its derivatives, unregulated order handling significantly increases energy consumption due to frequent door openings in [...] Read more.
This study investigates the efficiency of cold storage warehouses and contributes to sustainable supply chain management by integrating eco-friendly practices into storage operations. In facilities for milk and its derivatives, unregulated order handling significantly increases energy consumption due to frequent door openings in the cooler. To address this challenge, we developed a novel mathematical model aimed at optimizing order sequences and minimizing energy costs, addressing a previously unexplored gap in the literature. A genetic algorithm (GA) was employed to solve this model, with careful consideration of carbon emissions generated during the algorithm’s execution. We utilized the Yates notation, an experimental design technique, to systematically optimize the GA’s parameters, ensuring robust and statistically valid results. This methodology enabled a thorough analysis of the factors influencing energy consumption. The findings enhance energy efficiency in cold storage warehouses, leading to reduced carbon dioxide emissions and fostering sustainable practices within supply chain management. Ultimately, this study successfully integrates green practices into cold storage operations, supporting broader sustainability objectives. Full article
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