Primary supervisorSarah Goodwin
- Guido Tack
- Lee Lawrence
Research areaData Visualisation and Immersive Analytics
Energy market operators make data-driven decisions via 24/7 control rooms with the use of many different applications across their multiple screen workstations. The types of decisions the operators are undertaking depend on the time of day and the state of the network. With the increase of data in recent years and the influx of distributed energy resources, the types of decisions and quantity of information needing to be looked at at a glance to make informed decisions is rapidly changing.
This project proposes optimal layouts of screen based applications based based on the situation at hand. The project aims to better understand operator workload and explore improvement to decision makings based on optimising the workspace layouts based on the specific tasks and applications needed for those tasks in hand. This project builds on our prior work in AEMO's control room to assess the cognitive load of control room operators. In this previous work, eye tracking data demonstrates that certain screens were used more often than others for different tasks, and that certain applications are dependent on the other for certain tasks, and these differ throughout the day. With designing optimal layouts based on the network situation we seek to enable the operator to make quicker and more informed decisions, especially in stressful scenarios.
Project hastags: #energy #zema #energyvis #optimisation #decision-making
- Eligible for the PhD program and Zema Scholarship (see below for funding link)
- Programming skills and optimisation knowledge
- Experience in qualitative and quantitative research methods
- Interest in the Energy Sector
- Experience of data visualisation, optimisation and/or behavioural science