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Advanced Machine Learning and Big Data Technologies for Smart Cities...
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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 3455

Special Issue Editors

Dr. Seyed Mahdi Miraftabzadeh

E-Mail Website
Guest Editor
Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Interests: machine learning; smart cities; renewable energy; smart mobility; e-mobility
Dr. Michela Longo

E-Mail Website
Guest Editor
Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Interests: electric vehicles; optimization algorithm; smart mobility; transportation sector; e-mobility; infrastructure
Special Issues, Collections and Topics in MDPI journals
Dr. Lucio Ciabattoni

E-Mail Website
Guest Editor
Industrial Engineering and Mathematical Sciences Department, Università Politecnica delle Merche, Via Brecce Bianche 12, 60131 Ancona, Italy
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

Published Papers (2 papers)

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Research

14 pages, 3592 KiB  
Article
Large-Scale Rooftop Solar Photovoltaic Power Production Potential Assessment: A Case Study for Tehran Metropolitan Area, Iran
by Babak Ranjgar and Alessandro Niccolai
Energies 2023, 16(20), 7111; https://doi.org/10.3390/en16207111 - 16 Oct 2023
Cited by 3 | Viewed by 1073
Abstract
The exponential growth of population and industries has brought about an increase in energy consumption, causing severe climatic and environmental problems. Therefore, the move towards green renewable energy is being ever more intensified. This study aims at estimating the rooftop solar power production [...] Read more.
The exponential growth of population and industries has brought about an increase in energy consumption, causing severe climatic and environmental problems. Therefore, the move towards green renewable energy is being ever more intensified. This study aims at estimating the rooftop solar power production for Tehran, the capital city of Iran, using a Geospatial Information System (GIS) to assess the big data of city building parcels. Tehran is faced with severe air pollution due to its excessive fossil fuel usage, and its electricity demand is increasing. As a result, this paper attempts to provide the quantified solar power potential of city roof tops for policymakers and authorities in order to facilitate decision-making in relation to integrating renewable energies into the power production infrastructure. The results shows that approximately 3000 GWh (more than 14% of the total electric energy consumption) of solar power can be produced by the rooftop PV installations in Tehran. The potential nominal power of rooftop PV installations is estimated to be more than 2000 MW, which is four times the current installed PV capacity of the whole country. The findings of the study suggest that there is great potential hidden on the rooftops of the city, which can be utilized to assist the power systems of the city in the longer run for a more sustainable future. Full article
(This article belongs to the Special Issue Advanced Machine Learning and Big Data Technologies for Smart Cities and Grids)
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19 pages, 4459 KiB  
Article
Adaptive Energy Management of Big Data Analytics in Smart Grids
by Rohit Gupta and Krishna Teerth Chaturvedi
Energies 2023, 16(16), 6016; https://doi.org/10.3390/en16166016 - 17 Aug 2023
Cited by 1 | Viewed by 1977
Abstract
The smart grid (SG) ensures the flow of electricity and data between suppliers and consumers. The reliability and security of data also play an important role in the overall management. This can be achieved with the help of adaptive energy management (AEM). This [...] Read more.
The smart grid (SG) ensures the flow of electricity and data between suppliers and consumers. The reliability and security of data also play an important role in the overall management. This can be achieved with the help of adaptive energy management (AEM). This research aims to highlight the big data issues and challenges faced by AEM employed in SG networks. In this paper, we will discuss the most commonly used data processing methods and will give a detailed comparison between the outputs of some of these methods. We consider a dataset of 50,000 instances from consumer smart meters and 10,000 attributes from previous fault data and 12 attributes. The comparison will tell us about the reliability, stability, and accuracy of the system by comparing the output of the various graphical plots of these methods. The accuracy percentage of the linear regression method is 98%; for the logistic regression method, it is 96%; and for K-Nearest Neighbors, it is 92%. The results show that the linear regression method applied gives the highest accuracy compared to logistic regression and K-Nearest Neighbors methods for prediction analysis of big data in SGs. This will ensure their use in future research in this field. Full article
(This article belongs to the Special Issue Advanced Machine Learning and Big Data Technologies for Smart Cities and Grids)
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