The 4th ACM International Workshop on
Big Data and Machine Learning for Smart Buildings and Cities (In conjunction with ACM BuildSys 24)

 
6th, Nov, 2024
Hangzhou, Zhejiang, China






Welcome to ACM BALANCES 2024

The proliferation of urban sensing, IoT, and big data in buildings, cities, and urban areas provides unprecedented opportunities to better understand and optimize transportation, energy and water networks, and how human behavior affects them (and is, in turn, affected by them). However, historically due to poor-quality data, limitations in algorithms, and computational bottlenecks, modeling urban-scale occupant behavior and its interactions with energy and transportation demand has proven to be quite challenging. Therefore, progress in developing data-driven techniques, which can work with enormous amounts of data that is increasingly available today, is needed to unlock its full potential. In order to realize this potential, BALANCES focuses on innovative data-driven methodologies that can be applied to model and optimize buildings and cities. Additionally, it also places a spotlight on two different IEA EBC Annexes: the IEA EBC Annex 81 on Data-Driven Smart Buildings, and the IEA EBC Annex 82 on Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems.

In doing so, the workshop aims to open up discussions on:
1. Big data modeling paradigms that could be applicable in building and urban science,
2. Requirements on the data collection infrastructure needed for these modeling paradigms,
3. Challenges faced by current modeling approaches, and
4. Future research directions to fully utilize building and urban big data.

BALANCES'24 will be held in conjunction with ACM BuildSys 2024.

Important Dates

Nov. 6, 2024

Workshop Day

Oct. 4, 2024

Camera Ready Submission

Sept. 29, 2024

Notification to Authors

Sept. 27, 2024

Reviewer Deadline

Sept. 18, 2024

Paper Submission Deadline

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
  • Machine learning for intelligent building control
  • AI-driven building automation
  • IoT enabled smart buildings and cities
  • Modeling of human mobility in cities
  • Urban sensing
  • Data-driven urban scale occupant behavior modeling
  • Data-driven energy flexibility modelling on building and city-scale
  • Fault-free data-driven building operation 
  • Large language model for built environment
  • Scaling up models to big data and large scale deployment
  • Fit-for-purpose data acquisition and modeling
  • Model standardization and benchmarking
  • Fault-free data-driven building operation
  • City-scale model scalability
  • Urban scale building energy modeling
  • Outdoor thermal comfort

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 2024

Program


TBA

Organization


Technical Program Committee


Workshop Chairs

Prof. Zhipeng Deng
zhipeng.deng@ucf.edu

University of Central Florida
USA

Prof. Bing Dong
bidong@syr.edu

Syracuse University
USA

Dr. Sicheng Zhan
szhan@nus.edu.sg

National University of Singapore
Singapore

Best Regards from the ACM BALANCES Team
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