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Artificial Intelligence for Smart Infrastructure Solutions

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 701

Special Issue Editor


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Guest Editor
Department of Computer Science, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-271 Opole, Poland
Interests: machine learning; deep learning; computer vision; pattern recognition; power systems maintenance; critical infrastructure monitoring; unmanned aerial vehicles; fault detection and classification

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is revolutionizing the way we approach and manage various aspects of modern infrastructure. From transportation networks and energy grids to urban planning and construction, AI is playing a pivotal role in optimizing efficiency, sustainability, and resilience. This Special Issue aims to explore innovative applications of AI in transforming and enhancing smart infrastructure solutions across different domains.

Researchers and practitioners are invited to submit original research articles, reviews, and case studies related to the integration of AI into various innovative infrastructure domains. Submissions should address practical challenges, showcase innovative solutions, and provide insights into the benefits and limitations of AI implementation.

Potential Topics:

  • Enhancing energy grids with AI: Explore AI-based solutions for improving the reliability and efficiency of energy distribution systems, demand forecasting, load balancing, and integration of renewable energy sources;
  • AI in construction and infrastructure monitoring: Examine how AI can be utilized in construction processes, structural health monitoring, remote sensing, faults detection and classification, and predictive maintenance of bridges, buildings, and other critical infrastructure;
  • Intelligent transportation systems: Discuss the role of AI in optimizing traffic management, reducing congestion, enhancing public transportation systems, and developing autonomous vehicles for safer and more efficient mobility;
  • Smart water management and AI: Highlight AI-powered strategies and methods for efficient water resource management, flood prediction, and water quality monitoring to ensure sustainable water infrastructure;
  • Resilient disaster response and recovery: Investigate the integration of AI into disaster response strategies, real-time data analysis, early warning systems, and post-disaster recovery efforts;
  • AI for sustainable building design: Explore AI's role in designing energy-efficient and sustainable buildings, optimizing HVAC (heating, ventilation, and air conditioning) systems, and improving indoor environmental quality;
  • Data analytics and decision support systems: Discuss the development of AI-driven analytics platforms and decision support systems that enable data-driven insights and informed decision-making for infrastructure planning and management;
  • AI-driven urban planning and smart Cities:**

    Investigate how AI algorithms and predictive modelling can be used to optimize urban planning, transportation systems, and overall urban development to create more livable and sustainable cities.

This Special Issue will bring together researchers, engineers, and experts from academia and industry to present and discuss the latest advancements in using AI for smart infrastructure solutions. It aims to foster knowledge exchange, promote interdisciplinary collaboration, and contribute to the development of sustainable and resilient smart cities and infrastructure networks.

Prof. Dr. Michal Tomaszewski
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

  • artificial intelligence
  • machine learning
  • deep learning
  • smart infrastructure
  • remote monitoring
  • infrastructure health monitoring
  • fault detection and classification
  • image and signal analysis
  • sensor networks
  • energy efficiency
  • resilience

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Published Papers (1 paper)

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Research

20 pages, 5221 KiB  
Article
Prediction of Intraday Electricity Supply Curves
by Guillermo Vivó and Andrés M. Alonso
Appl. Sci. 2024, 14(22), 10663; https://doi.org/10.3390/app142210663 - 18 Nov 2024
Viewed by 273
Abstract
The electricity market in Spain, as in many European countries, is organized into daily, intraday, and reserve markets. This project aims to predict the supply curves in the Spanish intraday market that have six sessions with different horizons of application, using information from [...] Read more.
The electricity market in Spain, as in many European countries, is organized into daily, intraday, and reserve markets. This project aims to predict the supply curves in the Spanish intraday market that have six sessions with different horizons of application, using information from the market itself. To achieve this, we approximate these curves using a non-uniform grid of points and evaluate the quality of these approximations with a weighted distance, both based on empirical market data. We employ neural network models, including multilayer perceptrons (MLPs), convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and a Transformer network alongside a naive model for benchmarking. The MLP and CNN models demonstrated significant improvements in predicting these supply curves for the six market sessions. Full article
(This article belongs to the Special Issue Artificial Intelligence for Smart Infrastructure Solutions)
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