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Artificial Intelligence for Energy Management in Buildings and Cities
08 Mar 2024 to 31 Jan 2025
Introduction

Buildings account for 40% of the global energy consumption and 30% of the associated greenhouse gas emissions, while also offering a 50–90% CO2 mitigation potential. Decarbonization strategies will require faster, tighter and more widely coordinated control of energy resources in buildings and cities at multiple spatiotemporal scales: from milliseconds and single components to decades and across geographic regions. The complexity of this problem necessitates automated processes to situational awareness and decision making. Artificial Intelligence approaches, in particular are regarded as a promising solution and are already being adopted in both academic and industry settings for energy management tasks. The purpose of this special collection in the open-access journal Data-Centric Engineering (Cambridge University Press; 2023 Impact Factor: 2.4) is to provide an up-to-date snapshot of the state of the art in data-driven solutions applied to energy management problems in the built environment. We are seeking contributions across all spatio-temporal scales, i.e., from smart water heater elements, to virtual power plants in cities.

We therefore call for contributions that highlight use cases in applying data-driven methods to improve the resiliency and efficiency of the building environment as engineering system, especially the energy systems, thus being in-line with the scope of DCE. This includes handling buildings energy system in coordination with other buildings, with grid needs, distrubuted energy resources, energy storage systems, and EV charging.

Topics
  • Challenges and Opportunities for AI in Buildings, grid, transportation, and Smart Cities
  • Explorations of model vs model-free algorithms and hybrids
  • Comparisons of algorithms for control tasks, i.e., MPC vs RL
  • Frameworks and datasets for benchmarking algorithms
  • Theoretical contributions brought about by constraints/challenges in the buildings cities domain
  • Human building interactions (occupant-centric controls, human integration, humanin-
    the-loop systems)
  • Applications (digital twins, demand response, HVAC control, traffic scheduling, EV/battery charging, DER integration)
  • Result and analysis of applying model-free and model-based algorithms in real world case studies
  • Smart, adaptive power electronics
  • Virtual power plants
Deadline

We encourage submissions as soon as possible with final deadline of:

  • August 30th, 2024 January 31, 2025

Articles will be published as soon as possible after acceptance and curated on a collection page dedicated to the topic with an editorial introducing the collection published at a later date.

Why submit to DCE?

✔ A venue dedicated to the potential of data science for all areas of engineering.
✔ Welcoming research and translational articles from authors, whether they are based in academia or industry.
✔ Well-cited (2023 Impact Factor: 2.4; 2023 Cite Score: 5.6) and indexed in Web of Science, Scopus and Directory of Open Access Journals.
✔ #OpenAccess with support for unfunded authors thanks to the Lloyd's Register Foundation - no hard requirement to pay an article processing charge (APC).
✔ Promotes open sharing of data and code through Open Science Badges.

How to submit

Key considerations for submitting are below, with full details available in the DCE Instructions for Authors. 

Article Types

DCE encourages the submission of: 

  • Research articles using data science methods and models for improving the reliability, resilience, safety, efficiency and usability of engineered systems.
  • Translational Articles from contributors based in industry or academic-industry collaborations and we believe this special issue holds a lot of potential for this type of contribution.
  • Data papers that describe in a structured way, with a narrative and accompanying metadata, important and re-usable data sets in open repositories with potential for re-use in engineering research and practice. These papers promote data transparency and data re-use
  • Survey papers providing a detailed, balanced and authoritative current account of the existing literature concerning data-intensive methods in a particular facet of engineering sciences.
  • Tutorial reviews providing an introduction and overview of an important topic of relevance to the journal readership. The topic should be of relevance to both students and researchers who are new to the field as well as experts and provide a good introduction to the development of a subject, its current state and indications of future directions the field is expected to take
  • Position papers providing an overview of an important issue for this emerging field. (Typically 6,000 words or less).

Templates

Authors have the option but are not required to use the following templates:


Articles should be submitted through the DCE ScholarOne Manuscript Central system, but note that if you use the Overleaf tool you can submit directly into the system without having to reupload files.

Open access

Any author can publish on an open access basis in DCE if accepted, irrespective of their funding situation or institutional affiliation. There are no financial barriers to publication. Many articles are covered through the Transformative Agreements that Cambridge has set up with universities worldwide. If the corresponding author on an article is affiliated with a Transformative Agreement this effectively covers open access publishing costs. Authors not affiliated with these agreements who have grants that budget for open access publication are encouraged to pay an article processing charge (APC). However, if an author has no funding and no institutional agreement, the charge will be waived without question. DCE is supported by a grant from the Lloyd’s Register Foundation, which helps subsidise the publishing costs of unfunded authors.

Open materials

Authors are encouraged to make code and data that supports the findings openly available in a recognised repository and to link to them in the Data Availability Statement in the article. We recognise this may not be possible in all circumstances. See the DCE Research Transparency policy. Open Data and Open Materials badges will be displayed on published articles that link to replication materials, as a recognition of open practices.

Cambridge’s Open Engage platform is a location for sharing early research outputs and additional materials. It can, for example, be used to host working papers, pre-prints, presentations and posters. Materials uploaded to Open Engage will receive a DOI (and therefore be citable objects), allowing authors to link to them in their submitted article.

Guest Editors
  • Zoltan Nagy, PhD, The University of Texas at Austin
  • Helia Zandi, Oak Ridge National Laboratory
  • June Young Park, PhD, The University of Texas at Arlington
  • Mario Bergés, PhD, Carnegie Mellon University