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

Document Type

Conference Proceeding

Publication Date

10-1-2020

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.

Identifier

85099576539 (Scopus)

ISBN

[9781728185149]

Publication Title

Proceedings 2020 IEEE Workshop on Trust and Expertise in Visual Analytics Trex 2020

External Full Text Location

https://doi.org/10.1109/TREX51495.2020.00007

First Page

16

Last Page

21

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