FATEsys 2022

The Second ACM SIGEnergy Workshop on Fair, Accountable, Transparent, and Ethical (FATE) AI for Smart Environments and Energy Systems
(A SenSys/BuildSys 2022 Workshop)
November 11, 2022
Boston, Massachusetts, USA
Register now via BuildSys 2022

About FATEsys 2022

Ubiquitous networked sensing, smart control, and efficient communication technologies have enabled “big” data generation which has led to an unprecedented adoption and growth of machine learning (ML) and artificial intelligence (AI) in the transformation of smart energy systems. ML/AI driven solutions are attaining new levels of accuracy and energy-efficiency for several problems related to smart energy systems. For the same reason, AI is anticipated to play an important role in achieving the goals of energy equity and environmental justice. However, while developing these models, it is equally important to hold the models accountable for the impact of their actions on humans in the loop. The second workshop on FATE of AI-Enabled Smart Energy Systems intends to foster discussion on both aspects of this topic and bring together researchers from diverse backgrounds to discuss challenges and breakthroughs in this multidisciplinary area of research.

Call for Papers

FATEsys 2022 Call for Papers (PDF)
The aim of this workshop is to create a platform for the SenSys/BuildSys community to discuss developing AI-enabled smart environments and energy systems that are not just accurate but also take responsibility for their actions. We invite submissions including, but not limited to:
  • AI for equitable energy systems in built environments, transportation, smart grids, and storage
  • Challenges in collecting representative data for fair training of the AI models
  • Studies exploring type of biases in energy-related data and their implications
  • Interpretable and explainable ML/AI models
  • Ethics for AI-enabled smart energy systems
  • Physics-informed ML for model interpretation
  • Socio-economic impact of using ML/AI in smart-energy systems
  • Challenges and solutions in sensing for smart energy systems
  • Accountability of ML/AI models in smart energy systems
  • Fair metrics for the evaluation of ML/AI methods
  • Exploring visual analytics for bias evaluation in data and models
  • Open source datasets, tools, and platforms to enable FATE AI

Submission Guidelines

Submitted papers must be unpublished and must not be currently under review for any other publication. Paper submissions must be at most 4 single-spaced US Letter (8.5"x11") pages, including figures, tables, and appendices (excluding references). All submissions must use the LaTeX (preferred) or Word styles found here. 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.

Submission link

All submissions must be in Adobe Portable Document Format (PDF) format through the HotCRP: https://fatesys22.hotcrp.com/

Important Dates

  • Paper submission: August 28 September 11, 2022 (AOE)
  • Notifications: September 30, 2022 (AOE)
  • Camera-ready: October 7, 2022 (AOE)
  • Workshop: November 9-10, 2022

Program

The workshop will be held in virtual format. All times in Eastern Time (GMT-4). Link to World Clock
8:00 - 8:10
Opening remarks
8:10 - 9:00
Yunchuan Liu
Keynote: Towards Automated Machine Learning on Imperfect Data for Situational Awareness in Power System
Speaker: Yunchuan Liu (Governors State University, Illinois, USA) is an Assistant professor with the Division of Science Mathematics and Technology at Governors State University. He received his Ph.D. degree from the Department of Computer Science and Engineering at University of Nevada, Reno, in 2022. His current research interests include machine learning and data analytics for energy system applications. His paper has won the Best Paper Award at IEEE PES General Meeting 2022.

Abstract: Thanks to the wide deployment of sensors (e.g., phasor measurement units (PMUs)), which provide golden opportunities to achieve high level of situational awareness for reliable and cost-effective grid operations. However, the data collected from the real-world power system is of low quality (e.g., noisy, missing, insufficient and inaccurate timestamp data). Employing Machine Learning (ML) without considering these distinct features in real-world applications cannot build good ML models. 2 main contributions of our work:1) A novel machine learning framework is proposed for robust event classification to address the low data quality issues; 2) a weakly supervised learning framework is proposed to address the challenge of insufficient training labels.
9:00 - 10:00
Technical paper session
  • GEIN: An interpretable benchmarking framework towards all building types based on machine learning
    Xiaoyu Jin, Fu Xiao, ChongZhang, and Ao Li (The Hong Kong Polytechnic University, Hong Kong)
  • Addressing Social Vulnerability in Community Microgrids: An Equity-Centered Peer-to-Peer Electricity Trading Model
    Nafiseh Ghorbani Renani, Danielle Preziuso, and Philip Odonkor (Stevens Institute of Technology, USA)
  • What insights can we draw from our residential energy models? Guidelines for future modelling exercises
    Matthias Heinrich (Gustave Eiffel University, France), Marie Ruellan (CY Cergy Paris University, France), Jean-Pierre Lévy (Gustave Eiffel University, France), Allou Samé (Gustave Eiffel University, France), and Latifa Oukhellou (Gustave Eiffel University, France)
  • Conceptual Design of a Digital Twin-Enabled Building Envelope Energy Audits and Multi-Fidelity Simulation Framework for a Computationally Explainable Retrofit Plan
    Yu Hou (Western New England University, USA), and Rebekka Volk (Karlsruhe Institute of Technology, Germany)
10:00 - 10:05
Closing remarks

Keynote Speaker

Yunchuan Liu
Title: Towards Automated Machine Learning on Imperfect Data for Situational Awareness in Power System
Speaker: Yunchuan Liu (Governors State University, Illinois, USA) is an Assistant professor with the Division of Science Mathematics and Technology at Governors State University. He received his Ph.D. degree from the Department of Computer Science and Engineering at University of Nevada, Reno, in 2022. His current research interests include machine learning and data analytics for energy system applications. His paper has won the Best Paper Award at IEEE PES General Meeting 2022.

Abstract: Thanks to the wide deployment of sensors (e.g., phasor measurement units (PMUs)), which provide golden opportunities to achieve high level of situational awareness for reliable and cost-effective grid operations. However, the data collected from the real-world power system is of low quality (e.g., noisy, missing, insufficient and inaccurate timestamp data). Employing Machine Learning (ML) without considering these distinct features in real-world applications cannot build good ML models. 2 main contributions of our work:1) A novel machine learning framework is proposed for robust event classification to address the low data quality issues; 2) a weakly supervised learning framework is proposed to address the challenge of insufficient training labels.

Organization

General Co-chairs

Milan Jain
Milan Jain
Pacific Northwest National Laboratory
Richland, WA, USA
Pandarasamy Arjunan
Pandarasamy Arjunan
Berkeley Education Alliance for Research in Singapore (BEARS)
Singapore

Technical Program Committee

Venue

For venue details, visa information, etcetera please visit the BuildSys venue page