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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 10792

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Special Issue Editor

Dr. Mohammad Heidarinejad

E-Mail Website1 Website2
Guest Editor

Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: building science; modeling of the built environment; building energy and environmental systems; energy-efficient buildings; building energy simulations; building control; computational fluid dynamics; indoor air quality; sustainable and smart cities

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advances in modeling building energy and environmental systems in the built environment. The scope of this Special Issue entails modeling building energy and environmental systems at different scales ranging from the building components (e.g., blinds, light fixtures) to city-scale modeling. This issue welcomes submissions that verify and validate their models with measured data or on-site collected data. Possible topics of interest are:

Developing next generation of building energy and environmental systems modeling tools to design, operate, and retrofit buildings;
Establishing novel building energy modeling techniques to account for the building scale, urban scale, district scale, and city scale;
Leveraging building information modeling (BIM), geographic information system (GIS), industrial foundation classes (IFC), digital twins, or similar tools to model building energy and environmental systems in the built environment;
Advancing our modeling techniques to quantify impacts of occupancy patterns and behaviors in residential and commercial buildings;
Developing integrated modeling platforms to operate and control building components and systems (e.g., light fixtures, blinds, and smart thermostats, and heating, ventilation, and air conditioning (HVAC) systems);
Utilizing data-driven methods to study building operation under different existing and future constraints (e.g., dynamic utility rates, future climate scenarios, and future emission tax rates);
Considering application of artificial intelligence (e.g., machine learning and semantic modeling) in modeling building energy and environmental systems;
Using optimization algorithms to enhance performance of building energy and environmental systems;
Benefiting from an integrated life cycle analysis (LCA) to account for the impacts of new building materials on the energy, cost, and environmental impacts of buildings.

The aim of this issue is to bring together experts from different disciplines to summarize recent advances in modeling building energy and environmental systems in the built environment and to demonstrate that effectiveness of these modeling techniques that meet the validation and verification requirements.

Dr. Mohammad Heidarinejad
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. 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

modeling of the built environment
building energy and environmental systems
energy-efficient buildings
building energy simulations
building control
sustainable and smart cities

Published Papers (4 papers)

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Research

18 pages, 1382 KiB

Open AccessArticle

A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning

by Parastoo Delgoshaei, Mohammad Heidarinejad and Mark A. Austin

Sustainability 2022, 14(10), 5810; https://doi.org/10.3390/su14105810 - 11 May 2022

Cited by 7 | Viewed by 2330

Abstract

Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems. Full article

(This article belongs to the Special Issue Modeling Building Energy and Environmental Systems in the Built Environment)

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20 pages, 1435 KiB

Open AccessArticle

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

by Amal A. Al-Shargabi, Abdulbasit Almhafdy, Dina M. Ibrahim, Manal Alghieth and Francisco Chiclana

Sustainability 2021, 13(22), 12442; https://doi.org/10.3390/su132212442 - 11 Nov 2021

Cited by 10 | Viewed by 2008

Abstract

The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R

^{2}

). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R

^{2}

both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R

^{2}

both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings. Full article

(This article belongs to the Special Issue Modeling Building Energy and Environmental Systems in the Built Environment)

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20 pages, 5473 KiB

Open AccessArticle

Comparative Modelling Analysis of Air Pollutants, PM_2.5 and Energy Efficiency Using Three Ventilation Strategies in a High-Rise Building: A Case Study in Suzhou, China

by Nuodi Fu, Moon Keun Kim, Bing Chen and Stephen Sharples

Sustainability 2021, 13(15), 8453; https://doi.org/10.3390/su13158453 - 28 Jul 2021

Cited by 6 | Viewed by 2636

Abstract

This study investigated the ventilation efficiency and energy performance of three ventilation strategies—an all-air system (AAS), a radiant panel system with a displacement ventilation system (DPS), and a radiant panel system with a decentralized ventilation system (DVS). The research analyzed the indoor air quality (IAQ) in a high-rise building based on the building’s height, the air handling unit (AHU) location, air infiltration rate, outdoor air pollution rate, seasonal change, and air filter efficiency. The results indicated that the AAS had the best performance in terms of IAQ in the high-rise building in winter; however, the AAS also had the highest annual energy demand. For the same conditions, the DVS consumed less energy but had the worst performance in maintaining a satisfactory IAQ. Considering energy consumption, it is worth developing the DVS further to improve ventilation performance. By applying a double-filter system on the lower floors in a high-rise building, the DVS’s ventilation performance was dramatically improved while at the same time consuming less energy than the original DPS and AAS. The application of DVS can also minimize the negative effect of the infiltration rate on indoor air quality (IAQ) in a building, which means that the DVS can better maintain IAQ within a healthy range for a more extended period. Moreover, it was found that the DVS still had a substantial potential for saving energy during the season when the outdoor air was relatively clean. Hence, it is highly recommended that the DVS is used in high-rise buildings. Full article

(This article belongs to the Special Issue Modeling Building Energy and Environmental Systems in the Built Environment)

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18 pages, 5938 KiB

Open AccessArticle

Optimal Design Strategy of a Solar Reflector Combining Photovoltaic Panels to Improve Electricity Output: A Case Study in Calgary, Canada

by Moon Keun Kim, Khalid Osman Abdulkadir, Jiying Liu, Joon-Ho Choi and Huiqing Wen

Sustainability 2021, 13(11), 6115; https://doi.org/10.3390/su13116115 - 28 May 2021

Cited by 11 | Viewed by 2803

Abstract

This study explores the combination of photovoltaic (PV) panels with a reflector mounted on a building to improve electricity generation. Globally, PV panels have been widely used as a renewable energy technology. In order to obtain more solar irradiance and improve electricity output, this study presents an advanced strategy of a reflector combining PV panels mounted on a building in Calgary, Canada. Based on an experimental database of solar irradiances, the simulation presents an optimal shape designed and tilt angles of the reflector and consequently improves solar radiation gain and electricity outputs. Polished aluminum is selected as the reflector material, and the shape and angle are designed to minimize the interruption of direct solar radiation. The numerical approach demonstrates the improvement in performance using a PV panel tilted at 30°, 45°, 60°, and 75° and a reflector, tilted at 15.5° or allowed to be tilted flexibly. A reflector tilted at 15.5° can improve solar radiation gains, of the panel, by nearly 5.5–9.2% at lower tilt angles and 14.1–21.1% at higher tilt angles. Furthermore, the flexibly adjusted reflector can improve solar radiation gains on the PV panel, by nearly 12–15.6% at lower tilt angles and 20–26.5% at higher tilt angles. A reflector tilted at 15.5° improves the panel’s output electricity on average by 4–8% with the PV panel tilted at 30° and 45° respectively and 12–19% with the PV panel tilted at 60° and 75°, annually. Moreover, a reflector that can be flexibly tilted improves electricity output on average by 9–12% with the PV panel tilted at 30° and 45° and 17–23% with the PV panel tilted at 60° and 75°. Therefore, the utilization of a reflector improves the performance of the PV panel while incurring a relatively low cost. Full article

(This article belongs to the Special Issue Modeling Building Energy and Environmental Systems in the Built Environment)

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