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01661nam a2200253Ia 4500 |
| 001 |
10.3389-fdata.2022.897295 |
| 008 |
220718s2022 CNT 000 0 und d |
| 020 |
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|a 2624909X (ISSN)
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| 245 |
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|a A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis
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| 260 |
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|b Frontiers Media S.A.
|c 2022
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.3389/fdata.2022.897295
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|a This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender. Copyright © 2022 Wenskovitch, Jefferson, Anderson, Baweja, Ciesielski and Fallon.
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|a cognitive load
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|a contingency analysis
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|a human-machine teaming
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|a power grid
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|a trust evaluation
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| 700 |
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|a Anderson, A.
|e author
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| 700 |
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|a Baweja, J.
|e author
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|a Ciesielski, D.
|e author
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|a Fallon, C.
|e author
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|a Jefferson, B.
|e author
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| 700 |
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|a Wenskovitch, J.
|e author
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| 773 |
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|t Frontiers in Big Data
|x 2624909X (ISSN)
|g 5
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