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Artificial Intelligence (AI) in Smart Buildings
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Artificial Intelligence (AI) in Smart Buildings

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 14181

Special Issue Editor

Prof. Dr. Anca Ralescu

E-Mail Website
Guest Editor
Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
Interests: artificial intelligence; machine learning including deep learning; causal inference; fuzzy systems; image understanding; brain computer interface; assistive robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart building refers to any structure that uses automated processes to automatically control the operation of the building, including heating, ventilation, air conditioning, lighting, security, and other systems. In recent years, artificial intelligence technologies have been widely used in smart buildings, which improve the operation and management efficiency of buildings. At the same time, these technologies can also improve the safety and amenity of building users.

The Journal of Applied Sciences is calling for the submission of articles to a Special Issue on Smart Cities.

Topics to be addressed include:

  • Cyber-security and data privacy
  • Data visualization
  • Intelligent infrastructure
  • Intelligent public transportation systems
  • Intelligent traffic systems 
  • Internet of Things (IoT)
  • Modeling and simulation of smart city systems
  • Sensors
  • Smart city analytics
  • Theoretical underpinnings of smart city systems, including:
    • Artificial intelligence
    • Computational intelligence
    • Machine learning and pattern recognition

Prof. Dr. Anca L. Ralescu
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. Applied Sciences 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

  • Cyber-security & Data Privacy
  • Intelligent Infrastructure
  • Intelligent public transportation systems
  • Intelligent traffic systems
  • Smart City Analytics

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Published Papers (3 papers)

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Research

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16 pages, 3560 KiB  
Article
A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
by Deyslen Mariano-Hernández, Luis Hernández-Callejo, Martín Solís, Angel Zorita-Lamadrid, Oscar Duque-Perez, Luis Gonzalez-Morales and Felix Santos-García
Appl. Sci. 2021, 11(17), 7886; https://doi.org/10.3390/app11177886 - 26 Aug 2021
Cited by 16 | Viewed by 3322
Abstract
Smart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building’s energy consumption. With the help of forecasting models, building energy management systems, which [...] Read more.
Smart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building’s energy consumption. With the help of forecasting models, building energy management systems, which are a fundamental part of smart buildings, know when sudden changes in the energy consumption pattern could occur. Currently, different forecasting methods use models that allow building energy management systems to forecast energy consumption. Due to this, it is increasingly necessary to have appropriate forecasting models to be able to maintain a balance between energy consumption and occupant comfort. The objective of this paper is to present an energy consumption forecasting strategy that allows hourly day-ahead predictions. The presented forecasting strategy is tested using real data from two buildings located in Valladolid, Spain. Different machine learning and deep learning models were used to analyze which could perform better with the proposed strategy. After establishing the performance of the models, a model was assembled using the mean of the prediction values of the top five models to obtain a model with better performance. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Smart Buildings)
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25 pages, 4691 KiB  
Article
Smart Cities and Data Analytics for Intelligent Transportation Systems: An Analytical Model for Scheduling Phases and Traffic Lights at Signalized Intersections
by Fatih Gunes, Selim Bayrakli and Abdul Halim Zaim
Appl. Sci. 2021, 11(15), 6816; https://doi.org/10.3390/app11156816 - 24 Jul 2021
Cited by 5 | Viewed by 2983
Abstract
This paper is intended to improve the performance of signalized intersections, one of the most important systems of traffic control explained within the scope of smart transportation systems. These structures, which have the main role in ensuring the order and flow of traffic, [...] Read more.
This paper is intended to improve the performance of signalized intersections, one of the most important systems of traffic control explained within the scope of smart transportation systems. These structures, which have the main role in ensuring the order and flow of traffic, are alternative systems depending on the different methods and techniques used. Determining the operation principles of these systems requires an extremely careful and planned study, considering their important role. Performance outputs obtained from the queue analyses made in previous studies formed the input of this study. The most important techniques are used in the effective control of intersections, such as signal timing: in particular, the use of effective green time and order of the transitions between phases. In this research, a traffic-sensitive signalized intersection control system was designed with the suggested methods against these two problems. The sample intersections were selected from three cities with the highest population density as the case study area. In the analysis of the performance of the connection arms of the selected intersections, flow intensity data were taken into consideration, as well as the arrival and service rates. Based on this, the outputs of the two proposed models regarding the use of phase transitions and effective green durations were compared with two other adaptive control systems and their positive results were shared. The results showed that signalized intersections, operating with a well-planned and correctly chosen technique, better regulate density and queuing. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Smart Buildings)
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Review

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21 pages, 1274 KiB  
Review
Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
by Chrysanthos Maraveas, Dimitrios Loukatos, Thomas Bartzanas and Konstantinos G. Arvanitis
Appl. Sci. 2021, 11(16), 7716; https://doi.org/10.3390/app11167716 - 22 Aug 2021
Cited by 11 | Viewed by 6541
Abstract
Artificial intelligence applications in fire safety of agricultural structures have practical economic and technological benefits on commercial agriculture. The FAO estimates that wildfires result in at least USD 1 billion in agriculture-related losses due to the destruction of livestock pasture, destruction of agricultural [...] Read more.
Artificial intelligence applications in fire safety of agricultural structures have practical economic and technological benefits on commercial agriculture. The FAO estimates that wildfires result in at least USD 1 billion in agriculture-related losses due to the destruction of livestock pasture, destruction of agricultural buildings, premature death of farm animals, and general disruption of agricultural activities. Even though artificial neural networks (ANNs), genetic algorithms (GAs), probabilistic neural networks (PNNs), and adaptive neurofuzzy inference systems (ANFISs), among others, have proven useful in fire prevention, their application is limited in real farm environments. Most farms rely on traditional/non-technology-based methods of fire prevention. The case for AI in agricultural fire prevention is grounded on the accuracy and reliability of computer simulations in smoke movement analysis, risk assessment, and postfire analysis. In addition, such technologies can be coupled with next-generation fire-retardant materials such as intumescent coatings with a polymer binder, blowing agent, carbon donor, and acid donor. Future prospects for AI in agriculture transcend basic fire safety to encompass Society 5.0, energy systems in smart cities, UAV monitoring, Agriculture 4.0, and decentralized energy. However, critical challenges must be overcome, including the health and safety aspects, cost, and reliability. In brief, AI offers unlimited potential in the prevention of fire hazards in farms, but the existing body of knowledge is inadequate. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Smart Buildings)
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