Advanced Machine Learning and Big Data Technologies for Smart Cities and Grids
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".
Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 4758
Special Issue Editors
Interests: machine learning; smart cities; renewable energy; smart mobility; e-mobility
Interests: electric vehicles; optimization algorithm; smart mobility; transportation sector; e-mobility; infrastructure
Special Issues, Collections and Topics in MDPI journals
Interests: energy management; artificial intelligence; intelligent control; cyber physical systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Smart city design emphasizes the efficient management of challenges arising from urbanization, energy consumption, environmental conservation, and economic development while simultaneously enhancing the quality of life for citizens through the adoption of contemporary information and communication technology (ICT). These cities rely on a network of interconnected devices, sensors, and systems that collect and process real-time data to optimize the infrastructure and services of the city. By integrating advanced technologies and citizen engagement, smart cities aim to create sustainable, efficient, and livable urban environments. Machine learning algorithms can be used to analyze large amounts of data from sensors, cameras, and other sources in real time, and to gain insights into the traffic flow, energy consumption patterns, cybersecurity, safety, intelligent transportation systems (ITSs), and other vital metrics.
This Special Issue aims to present and disperse the most recent advances related to applications of machine learning and big data technologies in smart cities.
Topics of interest for publication include, but are not limited to:
- Energy management: machine learning algorithms can be utilized to optimize energy consumption, forecast energy demand, and identify areas for energy conservation in buildings and smart grids.
- Intelligent transportation systems: machine learning and big data algorithms can be used to analyze real-time data from traffic sensors, cameras, and other sources to optimize traffic flow, reduce congestion, and improve road safety.
- Electrification of the transportation system: machine learning algorithms can be used to optimize the electrification of transportation systems by enabling predictive maintenance, battery management, charging infrastructure optimization, and route optimization, as well as by improving vehicle performance and efficiency.
- Energy-efficient utilization of smart grids: machine learning algorithms can be used to predict energy demand; optimize energy generation and distribution; detect and predict faults in the grid, load forecasting, and energy theft detection; and facilitate demand response programs.
- Climate change: machine learning can help improve the integration and efficiency of renewable energy sources, such as wind and solar power, by predicting their availability and optimizing their use.
- Review papers covering the state of the art of the literature on advances in machine learning and big data technologies applications for smart cities and grids.
Dr. Seyed Mahdi Miraftabzadeh
Dr. Michela Longo
Dr. Lucio Ciabattoni
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
- machine learning
- deep learning
- big data
- smart cities
- smart grids
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.