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Smart Cities
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Energy Strategies of Smart Cities, 2nd Edition
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Energy Strategies of Smart Cities, 2nd Edition

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Smart Urban Energies and Integrated Systems".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 3022

Special Issue Editors

Prof. Dr. George Cristian Lazaroiu

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Guest Editor
Department of Power Systems, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania
Interests: smart cities; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Mariacristina Roscia

E-Mail Website
Guest Editor
Department of Engineering and Applied Sciences, University of Bergamo, Bergamo, Italy
Interests: smart cities; Internet of Things; smart grids; intelligent transportation
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel G. Costa

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering (DEEC), Faculty of Engineering (FEUP), University of Porto, Porto, Portugal
Interests: smart cities; Internet of Things; sensor networks; embedded systems; multimedia sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The upcoming Special Issue on energy strategies for smart cities aims to attract high-quality writings that address the increasingly complex energy challenges that urban environments face as they simultaneously manage rapid population growth, ambitious climate change mitigation goals, and large-scale digitization of critical infrastructure. As cities evolve into interconnected socio-technical systems, new energy strategies are now based on advanced digital technologies, AI-based analytics, and multi-vector coordination frameworks, which together increase efficiency, sustainability, equity, and environmental performance in urban areas.

In particular, we encourage publications that explore innovative configurations of distributed energy resources; active participation of citizens in the energy transition, which should be informed and trained for building an innovative and inclusive model of sustainable city; intelligent control of electric thermal mobility systems; and the integration of electric vehicles as dynamic assets for the energy architectures of future cities. Equally welcome are contributions on cybersecurity and data governance mechanisms, which are still essential to provide reliable digital energy platforms that are able to support real-time action and broad citizen participation. In addition, studies focusing on policy design, social inclusion, economic evaluations, and cross-sectoral governance models are welcome, as these dimensions are essential to transform technological development into functional and socially sustainable urban strategies.

Through interdisciplinary research and global case studies, this Special Issue aims to provide a holistic perspective on how cities can implement innovative, equitable, and efficient energy strategies that accelerate the transition to smarter, more sustainable, and more resilient futures.

Prof. Dr. George Cristian Lazaroiu
Dr. Mariacristina Roscia
Dr. Daniel G. Costa
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 250 words) can be sent to the Editorial Office for assessment.

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. Smart Cities is an international peer-reviewed open access monthly 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 2000 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

  • energy strategy
  • smart cities
  • technical innovation
  • engineering development
  • artificial-intelligence-driven energy systems
  • policy strategies
  • urban planning
  • sustainable development
  • climate change
  • Internet of Things
  • data management
  • business cases
  • social behavior
  • inclusive innovation
  • interdisciplinary development
  • governance frameworks

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (4 papers)

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Research

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17 pages, 1012 KB  
Article
Energy Consumption Forecasting in Public Nursing Homes Using Multivariable Regression Models
by Miguel Gómez-Chaparro, Alejandro Prieto-Fernández, Manuel Botejara-Antúnez and Justo García-Sanz-Calcedo
Smart Cities 2026, 9(5), 79; https://doi.org/10.3390/smartcities9050079 - 30 Apr 2026
Viewed by 329
Abstract
Buildings represent 40% of the European Union’s energy consumption and 36% of its greenhouse gas emissions. Nursing homes are among the buildings that consume the most energy. The objective of this study was to make predictive models of Energy Consumption, Energy Costs, and [...] Read more.
Buildings represent 40% of the European Union’s energy consumption and 36% of its greenhouse gas emissions. Nursing homes are among the buildings that consume the most energy. The objective of this study was to make predictive models of Energy Consumption, Energy Costs, and CO2 Emissions in nursing homes using different variables. To do this, data from 20 public nursing homes located in Extremadura (Spain) during the 2019–2023 period were analyzed. All the buildings were built or renovated between 1995 and 2009; the useful area and the number of residents were in the range of 1332–10,880 m2 and 24–254 residents. A statistical analysis was performed using multivariable linear regression. During the research, equations that allow for the estimation of the annual Energy Consumption, Energy Costs and CO2 Emissions of nursing homes, according to the useful area and number of residents, were found. The Radj2 was 0.9710, 0.9744 and 0.9742, respectively. The quality of the models obtained was contrasted using the mean absolute error (MAE), the relative error (RE) and the root mean square error (RMSE), together with the assessment of multicollinearity through the Variance Inflation Factor (VIF). The findings of this study may prove beneficial for stakeholders within the elder care sector. Full article
(This article belongs to the Special Issue Energy Strategies of Smart Cities, 2nd Edition)
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32 pages, 4751 KB  
Article
Advanced Multivariate Deep Learning Methodology for Forecasting Wind Speed and Solar Irradiation
by Md Shafiullah, Abdul Rahman Katranji, Mannan Hassan, Md Mahfuzur Rahman and Sk. A. Shezan
Smart Cities 2026, 9(4), 59; https://doi.org/10.3390/smartcities9040059 - 27 Mar 2026
Cited by 1 | Viewed by 1047
Abstract
The transition to smart cities is accelerating distributed wind and solar deployment. However, their intermittency challenges grid operation, thereby making accurate machine-learning-based prediction of wind speed and global horizontal irradiance (GHI) crucial. This study presents a cost-effective approach that enhances prediction accuracy by [...] Read more.
The transition to smart cities is accelerating distributed wind and solar deployment. However, their intermittency challenges grid operation, thereby making accurate machine-learning-based prediction of wind speed and global horizontal irradiance (GHI) crucial. This study presents a cost-effective approach that enhances prediction accuracy by extracting additional features from timestamp records for deep learning models used to forecast GHI and wind speed. Unlike conventional methods that require onsite meteorological measurements, the proposed approach uses only date and time information as inputs to multivariate deep neural networks, including recurrent neural networks, gated recurrent units, long short-term memory (LSTM), bidirectional LSTM, and convolutional neural networks. For wind speed prediction, the proposed configuration achieves R2 up to 0.9987, with RMSE as low as 0.067 m/s for 3 d ahead forecasting, outperforming univariate baselines and matching models. For GHI forecasting, the time-based configuration attains R2 values above 0.9994 in 12 h ahead predictions, with the RMSE reduced to approximately 4.47 W/m2, representing a substantial improvement over univariate models. The proposed framework maintains strong performance, particularly under clear and sunny conditions. These results demonstrate that timestamp-engineered features can deliver forecasting accuracy comparable to conventional multivariate meteorological models while significantly reducing infrastructure requirements, making the approach well-suited for scalable smart city energy management. Full article
(This article belongs to the Special Issue Energy Strategies of Smart Cities, 2nd Edition)
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Review

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17 pages, 643 KB  
Review
Feeder-Aware Coordination of Buildings, EVs, and DERs in Smart Cities: A Systematic Review of AI-, Digital-Twin-, and Interoperability-Enabled Approaches
by Manuel Dario Jaramillo, Diego Carrión and Alexander Aguila Téllez
Smart Cities 2026, 9(5), 87; https://doi.org/10.3390/smartcities9050087 - 20 May 2026
Viewed by 203
Abstract
Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. [...] Read more.
Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article
(This article belongs to the Special Issue Energy Strategies of Smart Cities, 2nd Edition)
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24 pages, 925 KB  
Review
GeoBIM for Geothermal Energy Efficiency in Buildings and Smart Cities: A Review
by Hugo Alexandre Silva Pinto, Luis M. Ferreira Gomes, Luis J. Andrade Pais, Miguel Nepomuceno, Luís Filipe Almeida Bernardo, Vanessa Gonçalves, Maria Vitoria Morais and Leonardo Marchiori
Smart Cities 2026, 9(3), 54; https://doi.org/10.3390/smartcities9030054 - 23 Mar 2026
Viewed by 882
Abstract
The global drive toward energy transition and carbon neutrality requires integrated and data-driven approaches for managing buildings and smart cities. Existing urban energy assessment frameworks remain fragmented and often lack multiscale interoperability between building-level models and territorial datasets. At the same time, shallow [...] Read more.
The global drive toward energy transition and carbon neutrality requires integrated and data-driven approaches for managing buildings and smart cities. Existing urban energy assessment frameworks remain fragmented and often lack multiscale interoperability between building-level models and territorial datasets. At the same time, shallow geothermal energy is emerging as an efficient and renewable solution for sustainable heating and cooling. To address these gaps, this study examines the potential of GeoBIM, the integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS), as a unified framework for multiscale energy analysis and for supporting shallow geothermal applications. A systematic literature review was conducted based on the PRISMA framework, combining a systematic literature review using the Scopus database with the critical examination of representative case studies. The results show that GeoBIM-based modeling improves data quality, enhances thermal performance assessments, and supports the implementation of shallow geothermal systems, including energy piles and district-scale ground-coupled networks. Reported applications demonstrate energy consumption reductions exceeding 40% in certain urban contexts. Several research gaps and challenges were identified, particularly data interoperability issues, lack of standardization, computational complexity, and the need for specialized training. Overall, the review indicates that GeoBIM offers a promising pathway for optimizing resources, supporting informed decision-making, and advancing resilient and sustainable smart buildings and cities. Full article
(This article belongs to the Special Issue Energy Strategies of Smart Cities, 2nd Edition)
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