Wenskovitch John, Jefferson Brett, Anderson Alexander, Baweja Jessica, Ciesielski Danielle, Fallon Corey
Pacific Northwest National Laboratory, National Security Directorate, Richland, WA, United States.
Pacific Northwest National Laboratory, Energy and Environment Directorate, Richland, WA, United States.
Front Big Data. 2022 Jun 14;5:897295. doi: 10.3389/fdata.2022.897295. eCollection 2022.
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.
这项工作展示了一种方法的应用，该方法用于测量领域专家的信任度和工作量、获取反馈，并了解在将机器学习助手引入实时电网仿真的应急分析时的技术可用性和影响。该框架的目标是快速收集和分析各种各样的人为因素数据，以加速机器学习应用部署的开发和评估循环。我们描述了我们的方法和分析，并讨论了从一名试点参与者那里获得的关于早期技术就绪水平（TRL）人工神经网络（ANN）推荐器当前可用性状态的见解。
