Smart and Sustainable Buildings: New Trends, Technologies, and Integration in the Energy Transition

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: 10 June 2025 | Viewed by 6292

Special Issue Editors


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Guest Editor
Engineering Department, University of Perugia, Perugia, Italy
Interests: buildings; smart city; renewables; electricity consumptions; lighting; numerical modelling; machine learning; optimization strategies; smart grids; energy community
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Information Engineering, University of Florence, Firenze, Italy
Interests: numerical modelling; renewable energy; neural networks; power converters; machine learning; optimization; embedded devices; circuits; energy storage; solar energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions to a Special Issue of Buildings entitled “Smart and Sustainable Buildings: New Trends, Technologies, and Integration in the Energy Transition”. Buildings’ energy consumption represents more than 40% of the energy produced worldwide: about the 50% of this energy is consumed in heating, cooling, and air conditioning (HVAC). Several milestones have been accomplished in the building technology sector in order to improve energy efficiency, with the use of innovative applications able to reduce energy consumption and enhance indoor environmental quality in buildings. These efforts are currently considered the most important challenge, together with the use of renewables. Building-integrated generation from renewable sources (BIPVs and thermal solar) represents a potential solution to reduce the carbon footprint of buildings. The generated energy can be used locally in the building, and the excess can be shared among the community or stored by means of electric-thermal storage solutions. The energy demand of the building can also be harmonized to achieve maximum self-consumption of energy, also exploiting efficient thermal and electrical storage for delayed use. Smart buildings should also be integrated into smart cities, connected to the electrical grid and sharing energy in a renewable energy community. The purpose of this Special Issue is to collect all of the recent and novel contributions, both in the form of theoretical and field-validated studies, on the following topics:

  • Efficient management of the building energy footprint;
  • Integration of energy generation from renewable sources in architecture and buildings;
  • Novel materials, technologies and building techniques for energy efficiency;
  • Thermal and environmental modelling of buildings;
  • IoT applications in the environmental, energetic, and anthropic monitoring of buildings;
  • Energy demand management and forecasting;
  • Machine learning and deep learning techniques applied to smart buildings;
  • Smart building integration in smart cities: EV mobility and renewable energy communities.

Dr. Elisa Belloni
Dr. Gabriele Maria Lozito
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. Buildings is an international peer-reviewed open access monthly 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 2600 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

  • smart buildings
  • sustainable buildings
  • renewables
  • BIPV (building-integrated photovoltaics)
  • building energy services
  • energy management
  • energy consumption forecasting
  • Internet of Things
  • machine learning for smart building energy data
  • grid integration and control

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Published Papers (4 papers)

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Research

21 pages, 6487 KiB  
Article
BIoT Smart Switch-Embedded System Based on STM32 and Modbus RTU—Concept, Theory of Operation and Implementation
by Ionel Zagan and Vasile Gheorghiță Găitan
Buildings 2024, 14(10), 3076; https://doi.org/10.3390/buildings14103076 - 26 Sep 2024
Viewed by 780
Abstract
Considering human influence and its negative impact on the environment, the world will have to transform the current energy system into a cleaner and more sustainable one. In residential as well as office buildings, there is a demand to minimize electricity consumption, improve [...] Read more.
Considering human influence and its negative impact on the environment, the world will have to transform the current energy system into a cleaner and more sustainable one. In residential as well as office buildings, there is a demand to minimize electricity consumption, improve the automation of electrical appliances and optimize electricity utilization. This paper describes the implementation of a smart switch with extended facilities compared to traditional switches, such as visual indication of evacuation routes in case of fire and acoustic alerts for emergencies. The proposed embedded system implements Modbus RTU serial communication to receive information from a fire alarm-control panel. An extension to the Modbus communication protocol, called Modbus Extended (ModbusE), is also proposed for smart switches and emergency switchboards. The embedded smart switch described in this paper as a scientific and practical contribution in this field, based on a performant microcontroller system, is integrated into the Building Internet of Things (BIoT) concept and uses the innovative ModbusE protocol. The proposed smart lighting system integrates building lighting access control for smart switches and sockets and can be extended to incorporate functionality for smart thermostats, access control and smart sensor-based information acquisition. Full article
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18 pages, 10887 KiB  
Article
The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing
by Zhenwei Guo, Xinyu Wang, Yao Wang, Fenglei Zhu, Haizhu Zhou, Miao Zhang and Yuxiang Wang
Buildings 2024, 14(10), 3040; https://doi.org/10.3390/buildings14103040 - 24 Sep 2024
Viewed by 1041
Abstract
Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm [...] Read more.
Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits. Full article
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16 pages, 2566 KiB  
Article
Establishing the Relationship between Occupants’ Thermal Behavior and Energy Consumption during Showering
by Dadi Zhang, Kwok-Wai Mui and Ling-Tim Wong
Buildings 2023, 13(5), 1300; https://doi.org/10.3390/buildings13051300 - 16 May 2023
Cited by 4 | Viewed by 1779
Abstract
Despite an increased awareness about energy conservation in the past decade, the energy consumed for water heating has increased by 7% from 2008 (17%) to 2018 (24%) in Hong Kong. A literature review on existing energy-saving technologies during showering showed that occupants’ behavior [...] Read more.
Despite an increased awareness about energy conservation in the past decade, the energy consumed for water heating has increased by 7% from 2008 (17%) to 2018 (24%) in Hong Kong. A literature review on existing energy-saving technologies during showering showed that occupants’ behavior significantly impacted energy consumption. However, the exact relationship between them was not yet fully understood. Therefore, this study developed a mathematical energy consumption model to investigate the relationship between occupants’ behavior and energy consumption during showering. This relationship identified an effective energy-saving strategy in the shower without scarifying occupants’ thermal comfort. The main variables that influence energy consumption and thermal comfort in bathrooms namely air temperature, water temperature, ventilation rate, and water flow rate, were considered. It was found that among them, water flow rate and ventilation rate are the most and least influential variables, respectively, in energy saving. Therefore, the ventilation rate was suggested to be at least 0.03 kg·s−1, and the water flow rate was meant to be lower than 0.15 kg·s−1 (based on related requirements). These findings could help residential occupants and facility managers determine the optimal showering settings for thermal comfort, energy consumption, and environmental effects. Full article
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27 pages, 4565 KiB  
Article
Greening Umbria’s Future: Investigation of the Retrofit Measures’ Potential to Achieve Energy Goals by 2030 in the Umbria Region
by Domenico Palladino
Buildings 2023, 13(4), 1039; https://doi.org/10.3390/buildings13041039 - 14 Apr 2023
Cited by 3 | Viewed by 1740
Abstract
The new European targets of achieving net zero emissions by 2050 have spurred Italy to aim for a 30% reduction in emissions by 2030, compared with 2005 levels. This goal will be achieved through the promotion of renewable energy sources and energy savings [...] Read more.
The new European targets of achieving net zero emissions by 2050 have spurred Italy to aim for a 30% reduction in emissions by 2030, compared with 2005 levels. This goal will be achieved through the promotion of renewable energy sources and energy savings in the residential sector, which remains one of the main sectors accountable for total energy consumption, mainly for heating. This study aims at investigating the potential of some retrofit measures implemented in the Umbria Region, chosen as a case study, to reach the goal by 2030. Using parametric energy simulations with the standard calculation method and artificial neural networks (ANN), the energy consumption of Umbria’s building stock and potential CO2 reductions were assessed. Results showed that with current energy policies, a reduction of 28% could be achieved, which is below the goal by 2030, while ANN integration within energy strategies could allow reaching it as early as 2025 or 2029, depending on the restriction set to the ANN and the extent of current energy policies. This study confirmed the potential benefits of using advanced technology in achieving national environmental goals, highlighting that they could be essential tools to be integrated into energy policies to accelerate progress towards ambitious climate goals. Full article
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