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Moving towards Digitalization in Building Energy Modeling

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 7213

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Special Issue Editors

Dr. Serdar Durdyev

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Guest Editor

Department of Engineering and Architectural Studies, Ara Institute of Canterbury, PO Box 540, Christchurch 8140, New Zealand
Interests: sustainable construction; construction supply chain; circular economy in construction; building information modelling
Special Issues, Collections and Topics in MDPI journals

Dr. Saeed Reza Mohandes

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Guest Editor

Department of Building and Real Estate (BRE), Faculty of Construction and Environment (FCE), The Hong Kong Polytechnic University, Hong Kong, China
Interests: artificial intelligence and machine learning in construction; construction safety; sustainable development; infrastructure management; building energy efficiency; facility management
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Prof. Dr. David John Edwards

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Guest Editor

School of Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, UK
Interests: digital technologies; business management; health and safety; pedagogical research in higher education; construction management; plant and machinery management
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Edmundas Kazimieras Zavadskas

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Guest Editor

Research Institute of Sustainable Construction, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania
Interests: operations research; optimization and decision analysis; multicriteria decision making; multiattribute decision making (MADM); decision support systems; civil engineering; energy; sustainable development; fuzzy sets theory; fuzzy multicriteria decision making; sustainability; management; game theory and economical computing knowledge management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As widely asserted, the construction sector is unarguably one of the biggest consumers of natural resources as compared to all the other industries. To cater for this problem, the idea of energy efficiency within the buildings came into the picture, and has grabbed the attention of myriad researchers and practitioners. Taking this background into consideration, the utilization of digital-based technologies is witnessed to have brought about drastic improvement in the modelling of buildings’ energy. Despite such proved blossoms, the exploitation of smart- and innovative-based technologies and techniques have lagged behind in this domain, making it naïve and immature as compared to the other sectors. Considering the aforementioned necessity, moving beyond the stagnant status quo for the further uptake of such technologies seems an undeniable fact, with a view to bringing about not only comforts for the residents living in buildings, but also to contributing towards our environment. Thus, this Special Issue aims to establish an open platform for the interested researchers in this stream to share their knowledge on the recent utilization of smart-based technologies (including Internet of things, building information modeling, digital twins, virtual/augmented reality, etc.) together with artificial intelligence-based techniques (including machine learning, artificial neural networks, fuzzy sets theories, stochastic optimizations), so as to improve the building energy modeling (BEM). This Special Issue also welcomes review papers compiling the exploitation of aforementioned technologies and techniques in the realm of BEM. Furthermore, studies undertaken on building up the linkages between the impact of COVID-19 and the BEM within the constructed facilities are favored.

Dr Serdar Durdyev
Dr Saeed Reza Mohandes
Prof. Dr. David J. Edwards
Prof. Dr. Edmundas Kazimieras Zavadskas
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. Energies 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

Building energy modeling
Building energy analysis
Cooling load prediction
Heating load prediction
Energy consumption
HVAC systems modeling
Optimizing energy efficiency
Artificial neural networks
Deep learning
Genetic algorithms
Fuzzy logic
Internet of things
Sensors
Virtual reality
Building information modeling
Stochastic optimization
Probabilistic simulation
Internet of things

Published Papers (2 papers)

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Research

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29 pages, 3577 KiB

Open AccessArticle

Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks

by Inoussa Laouali, Isaías Gomes, Maria da Graça Ruano, Saad Dosse Bennani, Hakim El Fadili and Antonio Ruano

Energies 2022, 15(23), 9073; https://doi.org/10.3390/en15239073 - 30 Nov 2022

Cited by 5 | Viewed by 1735

Abstract

Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices. Full article

(This article belongs to the Special Issue Moving towards Digitalization in Building Energy Modeling)

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Review

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15 pages, 1141 KiB

Open AccessReview

Review of the Building Information Modelling (BIM) Implementation in the Context of Building Energy Assessment

by Serdar Durdyev, Gholamreza Dehdasht, Saeed Reza Mohandes and David J. Edwards

Energies 2021, 14(24), 8487; https://doi.org/10.3390/en14248487 - 16 Dec 2021

Cited by 14 | Viewed by 4325

Abstract

In recent years, many researchers across the world have addressed the implementation of Building Information Modelling (BIM) in the energy assessment of the built environment. However, several potential issues still need to be resolved in order to utilise the benefits provided by BIM to a maximum degree. To fill this gap, a systematic literature review is conducted in this study to critically investigate the utilisation of BIM tools in energy assessment. To achieve the above-mentioned objective, after shortlisting the relevant papers published hitherto, using keyword searching, a systematic review was undertaken, including the application of BIM in the contexts of different countries, types of BIM tools, BIM and Life Cycle Assessment (LCA) integration, energy affiliations, stakeholders’ involvement and their roles, uncertainty, and sensitivity analysis. The outcomes show the most widely used and effective BIM tools in different types of construction projects in various countries. The review of the literature clearly shows that BIM tools can effectively be used in the assessment of energy performance of buildings. The article gives insight to engineers, architecture, and decision makers to carefully select appropriate BIM tools in terms of energy assessment. Full article

(This article belongs to the Special Issue Moving towards Digitalization in Building Energy Modeling)

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