The 1st ACM International Workshop on
Big Data and Machine Learning for Smart Buildings and Cities

In conjunction with 
ACM BuildSys 2021, Nov. 17-18, 2021
Coimbra, Portugal






Welcome to ACM BALANCES 2021

The proliferation of urban sensing, IoT, and big data in buildings, cities, and urban areas provides unprecedented opportunities for a deeper understanding of occupant behavior, transportation, and energy and water usage patterns. However, utilizing the existing data sources and modeling methods in building science to model urban scale occupant behaviors can be pretty challenging. Therefore, technological progress is needed to unlock its full potential. In order to fulfill the latter task, this workshop focuses on the methodologies for big urban and building data collection, analytics, modeling, and real-world technology deployment. Additionally, a spotlight will be put on the ongoing project IEA EBC Annex 79 Subtask 2: Data-driven occupant modeling strategies and digital tools.

The workshop aims to open up discussions on 1) challenges of the current modeling approaches in building science, 2) big data modeling paradigms that could be applicable in building science, urban infrastructure data modeling, 3) requirements on the data collection infrastructure required for increasing the volume of data collection, and 4) future research directions using urban big data. An important part of the workshop will be dedicated to accelerating the open-world deployment of developed technologies. For instance, how can the guidelines, model benchmarking, and standardization unlock the potential of the big data, buildings, urban scale occupant behavior modeling and energy consumption data.

BALANCES'21 will be held in conjunction with ACM BuildSys'21

Important Dates

(UTC-12:00)

Nov. 17-18, 2021

Workshop Day

Oct. 14, 2021

Camera Ready Submission

Sep. 25, 2021

Notification to Authors

Sep. 23, 2021

Reviewer Deadline

Sep. 18, 2021

Paper Submission

Call for Papers

The topics include, but are not limited to the following:

  • Machine learning for modeling big data from buildings, cities, and various urban-scale data
  • AI-driven building automation
  • Modeling of human mobility in cities
  • Urban sensing
  • Data-driven urban scale occupant behavior modeling
  • Scaling up models to big data and large scale deployment
  • Model standardization and benchmarking
  • Fault-free data-driven building operation 
  • City-scale model scalability
  • Urban scale building energy modeling
  • Outdoor thermal comfort
  • Big data for Grid-interactive efficient buildings (GEB)
  • Buildings-to-grid integration

Submission Guidelines

The workshop will accept the submissions of original work or work in progress. Submitted papers must be unpublished and must not be currently under review for any other publication. Submissions must be full papers, at most 4 single-spaced US Letter (8.5” x 11”) pages, including figures, tables, references and appendices. Submissions for All submissions must use the LaTeX (preferred) or Word styles found here. All submissions must be submitted using the submission website. Authors must make a good faith effort to anonymize their submissions by: (1) using the "anonymous" option for the class and (2) using "anonsuppress" section where appropriate. Papers that do not meet the size, formatting, and anonymization requirements will not be reviewed. Please note that ACM uses 9-pt fonts in all conference proceedings, and the style (both LaTeX and Word) implicitly define the font size to be 9-pt. 

The tentative presentation formats are regular oral presentation (15 minutes) and a spotlight presentation (2 minutes). 

Register through ACM BuildSys 2021

Program


Western European Summer Time (UTC+01:00)

TBD

Organization


Technical Program Committee

Workshop Chairs

Prof. Bing Dong
bidong@syr.edu

Syracuse University
USA

Prof. Salvatore Carlucci
s.carlucci@cyi.ac.cy

The Cyprus Institute
Cyprus

Dr. Ing. Romana Markovic
romana.markovic@kit.edu

Karlsruhe Institute of Technology
Germany


* Webmasters: Yapan Liu & Wei Mu

Best Regards from the ACM BALANCES Team
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