Computational Modelling and Data Analytics in Smart Cities—2nd Edition

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Information Applications".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 797

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Guest Editor
The e-Society Laboratory, College of Information Science and Engineering, Ritsumeikan University, Ibaraki 567-8570, Osaka, Japan
Interests: data science; IoT; e-society; statistical modelling and simulation; smart city and smart farming
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Special Issue Information

Dear Colleagues,

Goal 11 of the Sustainable Development Goals (SDGs), adopted by the United Nations General Assembly almost ten years ago, calls for making cities and human settlements inclusive, safe, resilient, and sustainable by 2030. As nations around the globe have been implementing various measures aimed at achieving this goal, it has quickly become evident that technological progress is the main vehicle and enabler of sustainability in urban settings. In particular, Artificial Intelligence (AI) with its rapidly growing array of models and methods opens up unparalleled opportunities not only for improving our daily life, but also for making cities “smart”—more self-reliant, greener, and simply a better place to live. Embracing this broad definition of “smart cities”, I would like to invite you to contribute to the Special Issue, reporting on recent advances and research findings that would move us closer to accomplishing Goal 11 of the SDGs. Original research, case studies, and survey papers are, therefore, solicited in the fields of AI, IoT, and Big Data on topics which include but are not limited to the following:

  • Smart city, smart infrastructure, and smart transportation (excluding research with a prime focus on self-driving cars);
  • Building information model (BIM), city information model (CIM), and digital twin city;
  • Smart tourism;
  • Digital governance (with the focus on municipal governments);
  • Disaster mitigation in urban areas;
  • Large language models (LLMs) and Artificial Intelligence-generated content (AIGC) for sustainable cities;
  • Crowdsourcing and open data in smart cities;
  • Green AI for sustainable communities.

I look forward to receiving your interesting contributions!

Prof. Dr. Victor Kryssanov
Guest Editor

Manuscript Submission Information

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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 1800 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

  • AI
  • IoT
  • big data
  • information model
  • smart cities

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Research

32 pages, 4417 KB  
Article
Operationalising an End-to-End MLOps Lifecycle for Energy Forecasting: Implementation and Controlled Evaluation on ClearML
by Xun Zhao, Zheng Grace Ma and Bo Nørregaard Jørgensen
Information 2026, 17(6), 576; https://doi.org/10.3390/info17060576 - 10 Jun 2026
Viewed by 453
Abstract
Operational energy-forecasting pipelines require traceable execution from data ingestion to monitoring, yet few studies evaluate whether such pipelines continue to enforce quality controls when inputs or configurations are degraded. This study implements a previously proposed seven-phase forecasting lifecycle as a configuration-driven system on [...] Read more.
Operational energy-forecasting pipelines require traceable execution from data ingestion to monitoring, yet few studies evaluate whether such pipelines continue to enforce quality controls when inputs or configurations are degraded. This study implements a previously proposed seven-phase forecasting lifecycle as a configuration-driven system on a self-hosted ClearML platform. The implementation is organised into five architectural domains: data and configuration, lifecycle phases and gates, orchestration, document artifact governance, and human-in-the-loop oversight. The pipeline is evaluated through six runs on four years of hourly electricity-consumption data from a Norwegian kindergarten building. Two baseline runs, in automatic and human-in-the-loop modes, demonstrate end-to-end execution and produce an XGBoost champion model with a 24-h-ahead test RMSE of 1.19 kW. Four controlled variants then test the validation-route logic by injecting missing data, shuffled consumption values, restrictive feature selection, and missing foundation-document sections. The first three variants are detected by phase-level sub-checkpoints, while the fourth is detected by Gate 0 through document-structure validation. The runs exercise revise-and-recover, override-then-terminate, and immediate-abort response pathways. The evaluation therefore demonstrates lifecycle execution, validation-route behaviour, and artifact traceability under controlled conditions; claims about live-deployment performance and multi-building generalisation are out of scope and identified as next steps. Full article
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