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Advances in Intelligent Building Management for Energy, Emission and Comfort
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Advances in Intelligent Building Management for Energy, Emission and Comfort

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: closed (20 October 2024) | Viewed by 2854

Special Issue Editor

Dr. Md Morshed Alam

E-Mail Website
Guest Editor
Centre for Sustainable Infrastructure and Digital Construction, Department of Civil and Construction Engineering ATC 734, Hawthorn Campus, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: digital construction; building information modelling; digital twin; construction management; building materials; building design and retrofit; energy rating; building performance and simulation; life cycle assessment; sustainable construction; sustainable building technology; life cycle cost analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The building sector accounts for 40% of total energy consumption globally. Therefore, reducing energy consumption in the building sector is crucial for environmental sustainability. Strategies to predict building energy consumption and enhance building energy performance are being investigated worldwide with different dynamic methods. However, a number of energy-efficient buildings are actually consuming more energy than predictions indicate. This is due to inexperienced building managers, non-adherence to building operational manuals, the degradation of building services and the lack of a feedback system to alert the facility manager to the potential misuse or overuse of energy.

Modern buildings are equipped with a Building Management System (BMS) that can schedule the operation of different service equipment and record the energy consumption of lighting, heating and cooling systems and mechanical and plug-in loads. However, it does not have the intelligence to analyze and identify energy waste, nor does it provide any feedback to the facility manager regarding consumption patterns or recognize any energy waste. The systematic analysis of these data sets to extract hidden knowledge and provide suggestions for improvement using artificial intelligence, machine learning and data analytics is an emerging science in the building sector. 

This Special Issue, “Advances in Intelligent Building Management for Energy, Emission and Comfort” aims to reflect the current state of the art and new developments in the application of artificial intelligence, machine learning and data analytics for intelligent building management to improve building energy efficiency, increase thermal comfort and reduce carbon emissions. This Special Issue will provide a comprehensive background for architects, building operational managers, building service engineers, researchers and experts in the field. Topics to be considered in this Special Issue include but are not limited to the keywords.

Dr. Md Morshed Alam
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. Buildings 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 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

  • supervised and unsupervised data analytics
  • building management system
  • energy consumption pattern
  • artificial intelligence
  • building operational data
  • building energy efficiency
  • machine learning
  • building automation systems

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Published Papers (2 papers)

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Research

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22 pages, 5007 KiB  
Article
As-Built Performance of Net-Zero Energy, Emissions, and Cost Buildings: A Real-Life Case Study in Melbourne, Australia
by Morshed Alam, William Graze, Tom Graze and Ingrid Graze
Buildings 2024, 14(11), 3614; https://doi.org/10.3390/buildings14113614 - 14 Nov 2024
Viewed by 819
Abstract
This research investigated the real-world operational performance of five purposely designed and built net-zero-energy houses in Melbourne, Australia. The embodied energy and carbon emissions of these houses were calculated based on their architectural and engineering drawings, as well as the relevant databases of [...] Read more.
This research investigated the real-world operational performance of five purposely designed and built net-zero-energy houses in Melbourne, Australia. The embodied energy and carbon emissions of these houses were calculated based on their architectural and engineering drawings, as well as the relevant databases of embodied energy and emission factors. Operational data, including solar production, consumption, end uses, battery usage, grid import, and grid export, were measured using the appropriate IoT devices from May 2023 to April 2024. The results showed that all the studied houses achieved net-zero energy and net-zero carbon status for operation, exporting between 3 to 37 times more energy than they consumed to the grid (except for house 2, where the consumption from the grid was zero). The embodied carbon of each case study house was calculated as 13.1 tons of CO2-e, which could be paid back within 4 to 9 years depending on the operational carbon. Achieving net-zero cost status, however, was found to be difficult due to the higher electricity purchase price, daily connection charge, and lower feed-in tariff. Only house 2 was close to achieving net zero cost with only AUD 37 out-of-pocket cost. Increasing the energy exported to the grid and storing the generated solar energy may help achieve net-zero cost. The installation of batteries did not affect the net-zero energy or emission status but had a significant impact on net-zero operational costs. However, the calculated payback period for the batteries installed in these five houses ranged from 43 to 112 years, making them impractical at this stage compared to the typical 10-year warranty period of the batteries. With rising electricity purchase prices, decreasing feed-in tariffs (potentially to zero in the future/already the case in some areas), and government incentives for battery installation, the payback period could be reduced, justifying their adoption. Moreover, the installed 13.5 kWh Tesla battery was too big for households with lower energy consumption like houses 2 and 5, which used only 25% of their total battery capacity most of the year. Therefore, selecting an appropriately sized battery based on household consumption could further help reduce the payback period. Full article
(This article belongs to the Special Issue Advances in Intelligent Building Management for Energy, Emission and Comfort)
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Review

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19 pages, 8160 KiB  
Review
A Review of Factors Affecting the Lighting Performance of Light Shelves and Controlling Solar Heat Gain
by Shadan Masoud, Zahra Zamani, Seyed Morteza Hosseini and Shady Attia
Buildings 2024, 14(6), 1832; https://doi.org/10.3390/buildings14061832 - 16 Jun 2024
Viewed by 1593
Abstract
In areas with a deep floor plan, the distribution of natural light is not uniform. Consequently, relying solely on daylight may not suffice to meet the space’s lighting requirements, necessitating the use of artificial lighting in darker areas. Therefore, a lighting system is [...] Read more.
In areas with a deep floor plan, the distribution of natural light is not uniform. Consequently, relying solely on daylight may not suffice to meet the space’s lighting requirements, necessitating the use of artificial lighting in darker areas. Therefore, a lighting system is needed that not only controls the glare near the windows but also increases the light at the end of the room and provides uniform daylight. One of the widely used systems is the “light shelf”, which has three main functions: shading, increasing the depth of light penetration, and reducing glare. Review articles about light shelves were published in 2015 and 2017, while more than 80% of the studies have been carried out since 2016, and light shelves with more diverse forms and dynamic elements and many consolidations have been proposed. Therefore, there is a need for a more comprehensive review. The main question of this research is how different parameters (including climate, material, ceiling, and integrated systems) can help to increase the efficiency of light shelves. By using a systematic review, studies in the past three decades were classified in order to determine the effect of these parameters on improving lighting performance and controlling solar heat gain. Full article
(This article belongs to the Special Issue Advances in Intelligent Building Management for Energy, Emission and Comfort)
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