@inproceedings{e71fec33381c492a9ce72b98e3b821e1,
title = "Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling",
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.",
keywords = "energy, explainability, grid, machine learning, transparency, trust",
author = "Kandakatla, {Akshith Reddy} and Vikas Chandan and Soumya Kundu and Indrasis Chakraborty and Kristin Cook and Aritra Dasgupta",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2020 ; Conference date: 25-10-2020",
year = "2020",
month = oct,
doi = "10.1109/TREX51495.2020.00007",
language = "English (US)",
series = "Proceedings - 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "16--21",
booktitle = "Proceedings - 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2020",
address = "United States",
}