Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era
Abstract
:1. Introduction
2. Materials and Methods
- A.
- Smart City/Cities + Machine Learning + 2015–2019 + articles + science technology = 170 publications.
- B.
- Smart City/Cities + Open Data + 2015–2019 + articles + science technology = 91 publications.
- − The content of the article was outside the scope of the study.
- − The paper did not describe a data application.
3. Results
- A bibliometric analysis of the publications database.
- A conceptual framework that provides an inclusive perspective on the applicability, domains and dimensions of sustainability, SDGs, tools, data types and machine learning techniques.
- A case study with the proposed model applied to the city of Malaga.
3.1. Bibliographic Results
3.1.1. Bibliometric Analysis
- Co-occurrence analysis: used to measure the co-occurrence of “keyword authors”, it bases its analysis on determining the number of documents in which they appear together.
- Bibliographic coupling analysis: this type of analysis was applied to the authors of publications. The relationship between authors is measured according to the number of bibliographic references they share among their publications.
- Co-authorship analysis: this analysis measures the relationship of the items based on the number of co-authorships in the documents. This type of analysis was applied to the reference organizations of the authors of the publications analyzed.
- Citation analysis: this analysis, performed for the “source” variable, i.e., journals and publishers in which the documents are published, determines the relationship based on the number of times they are cited.
- Co-citation analysis: applied to the “sources”, this analysis explains the relationships according to the number of times the “sources” are cited together.
3.1.2. Bibliographic Analysis for the Conceptual Framework Proposal
- Transport
- Energy
- Water and Air
- Location
- Social transformation.
3.2. A New Conceptual Framework for Implementing Machine Learning Techniques in Smart Cities. EARLY
3.2.1. Relationship SDG-Sustainability
3.2.2. Application of Sustainability–Data Relationship
3.2.3. Relationship between Sustainability Applications and Machine Learning Techniques
4. Discussion and Case Study
4.1. Contribution Discussion
- A bibliometric analysis of the publications database.
- A conceptual framework that provides an inclusive perspective on the applicability, domains and dimensions of sustainability, SDGs, tools, data types and machine learning techniques.
- A case study with the proposed model applied to the city of Malaga.
4.2. Case Study. Malaga City
4.2.1. Application
- Use of infrastructure.
- Fast charging at authorized points with exhaustive control.
- Energy optimization by using night hours to recharge electric vehicles.
- Active demand management, both for electric vehicle recharging processes and for other types of consumption.
- Quality of supply and service and guarantee of system stability.
4.2.2. SDGs
4.2.3. Data
4.2.4. Machine Learning
4.2.5. Evaluation
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Position | Organization | Number of Documents |
---|---|---|
1 | University of Ottawa | 6 |
2 | Sejong University | 4 |
3 | Huazhong University Science & technology | 3 |
4 | University of Florence | 3 |
5 | Nanyang Technology University | 3 |
6 | Northeastern University | 3 |
7 | University of Thessaly | 3 |
8 | University of Málaga | 3 |
Position | Organization | Citations |
---|---|---|
1 | La Trobe University | 93 |
2 | University of Ottawa | 88 |
3 | Imperial College of London | 65 |
4 | King Abdulaziz University | 57 |
5 | Canadian University of Dubai | 45 |
6 | Lulea University Technology | 44 |
7 | University of Edinburgh | 44 |
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De Las Heras, A.; Luque-Sendra, A.; Zamora-Polo, F. Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era. Sustainability 2020, 12, 9320. https://doi.org/10.3390/su12229320
De Las Heras A, Luque-Sendra A, Zamora-Polo F. Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era. Sustainability. 2020; 12(22):9320. https://doi.org/10.3390/su12229320
Chicago/Turabian StyleDe Las Heras, Ana, Amalia Luque-Sendra, and Francisco Zamora-Polo. 2020. "Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era" Sustainability 12, no. 22: 9320. https://doi.org/10.3390/su12229320