Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling

Akshith Reddy Kandakatla, Vikas Chandan, Soumya Kundu, Indrasis Chakraborty, Kristin Cook, Aritra Dasgupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The promise of data-driven predictive modeling is being increasingly realized in various science and engineering disciplines, where experts are used to the more conventional, simulation-driven modeling practices. However, trust remains a bottleneck for greater adoption of machine learning-based models for domain experts, who might not be necessarily trained in data science. In this paper, we focus on the building energy domain, where physics-based simulations are being complemented or replaced by machine learning-based methods for forecasting energy supply and demand at various spatio-Temporal scales. We study the trust problem in close collaboration with energy scientists and engineers and describe how visual analytics can be leveraged for alleviating this trust bottleneck for stakeholders with varying degrees of expertise and analytical goals in this domain.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages16-21
Number of pages6
ISBN (Electronic)9781728185149
DOIs
StatePublished - Oct 2020
Event2020 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2020 - Virtual, Salt Lake City, United States
Duration: Oct 25 2020 → …

Publication series

NameProceedings - 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2020

Conference

Conference2020 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2020
CountryUnited States
CityVirtual, Salt Lake City
Period10/25/20 → …

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Media Technology

Keywords

  • energy
  • explainability
  • grid
  • machine learning
  • transparency
  • trust

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