The 3rd ACM International Workshop on
Big Data and Machine Learning for Smart Buildings and Cities

In conjunction with 
ACM BuildSys 2023, November 14, 2023
Istanbul, Turkiye

Welcome to ACM BALANCES 2023

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'23 will be held in conjunction with ACM BuildSys'23

Important Dates


Nov. 14, 2023

Workshop Day

Oct. 8, 2023

Camera Ready Submission

Sept. 29, 2023

Notification to Authors

Sept. 27, 2023

Reviewer Deadline

Sept. 18, 2023

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
  • 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 
  • 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 2023


Workshop Schedule (November 14, 2023; All Times in TRT)


Closing Remarks


Presentation 5-8

10:30-10:45 On the causality of data-driven building thermal models
Fuyang Jiang, Johan Driesen, Hussain Kazmi

10:45-11:00 Enhancing Classification of Energy Meters with Limited Labels using a Semi-Supervised Generative Model
Chun Fu, Hussain Kazmi, Matias Quintana, Clayton Miller

11:00-11:15 Zone-Level Anomaly Detection in VAV Terminal Units Using an Unsupervised Learning Approach
Arya Parsaei, Burak Gunay, Liam OBrien, Ricardo Moromisato

11:15-11:30 The smart air purifier control strategy based on reinforcement learning for better IAQ and energy efficiency
Wenzhe Shang, Xilei Dai, Junjie Liu




Presentation 1-4

9:10-9:25 Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County
Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua New

9:25-9:40 PressureML: Modelling Pressure Waves to Generate Large-Scale Water-Usage Insights in Buildings
Tanmaey Gupta, Anupam Sobti, Akshay Nambi

9:40-9:55 Explainable AI for Energy Prediction and Anomaly Detection in Smart Energy Buildings
Hardik Prabhu, Pandarasamy Arjunan, Jayaraman Valdi

9:55-10:10 Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System
Gargya Gokhale, Jonas Van Gompel, Bert Claessens, Chris Develder


Opening Remarks



Technical Program Committee

Workshop Chairs

Prof. Bing Dong

Syracuse University

Dr. Ankush Chakrabarty

Mitsubishi Electric Research Laboratories

Dr. Xu Han

University of Kansas

Dr. Sicheng Zhan

National University of Singapore

Dr. Zhipeng Deng

Syracuse University

Best Regards from the ACM BALANCES Team
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