Honours and Masters project

Energy systems generate large volumes of heterogeneous information, including meter and sensor measurements, weather forecasts, electricity-market data, network models, operational procedures, technical reports and regulatory documents. Converting this information into timely and reliable decisions normally requires specialised modelling tools and considerable domain expertise.

Electricity markets are becoming increasingly dynamic as variable renewable generation, utility-scale batteries, distributed energy resources and flexible demand play larger roles in the energy system. Market participants must make decisions under uncertain prices, renewable generation, electricity demand, network conditions and market requirements while coordinating assets across multiple services and timescales.

The rapid growth of electric vehicles (EVs) is transforming both transportation and energy systems. Private EVs, commercial fleets, autonomous vehicles and shared-mobility services can contribute to cleaner urban transport, but their charging demand also creates new challenges for electricity networks, transport systems and charging-service providers.

Households are becoming active participants in the energy system. In addition to consuming electricity, many now generate, store and manage energy through consumer energy resources (CERs), including rooftop solar, household batteries, electric vehicles, smart appliances and flexible loads. At the same time, smart electricity, gas and water meters—as well as appliance, weather and other contextual data—are creating new opportunities for artificial intelligence to understand and improve household energy use.

Large Language Models (LLMs) are increasingly used as AI agents that can remember information across sessions, use tools, and take actions on behalf of users. For example, an AI agent may remember a user’s preferred contacts, coding conventions, project notes, research rules, or previous task instructions. This long-term memory can make agents more useful and personalised, especially for tasks that continue over time. However, long-term memory also introduces new security risks. An agent may remember

Large Language Model (LLM) agents are increasingly extended through external skills, tools, plugins, and reusable instruction modules. These skills allow agents to perform complex tasks such as file processing, web search, code execution, data analysis, and workflow automation. However, current security checks usually evaluate each skill in isolation. A skill may appear benign when tested alone, but become harmful when combined with other skills in the same agent workflow.

This project is an exciting opportunity to work on a multidisciplinary initiative aimed at improving the efficiency and effectiveness of energy control room operations. By focusing on human factors, best practices in visualisation, decision support, explainable AI, and optimisation, this project explores how to present information effectively for control room operators, ultimately contributing to the stability and efficiency of energy grid operations.

Background:

In our research we are reimagining the Control Room of the Future, where advanced data tools support better decision-making in complex environments like energy grid operations. A key focus of our research is understanding how operators interact visually with large-scale information displays. To do this, we use eye-tracking technology to capture detailed visual attention patterns, and synchronise it with workstation-level video and researcher notes. 

Agriculture is a significant contributor to nitrogen and phosphorus in waterways, impacting public health and ecosystems. Understanding and communicating the complex processes governing nutrient exports from agricultural catchments—regions where water collects and drains into a common outlet—are crucial for effective management and policy development. 

This project seeks to explore and trial new map morphing representations for seeing river water quality data sets more effectively over time and space. 

We are particularly focusing on the Melbourne and the region of Victoria, but expect the visualisation to be applicable to any geographical region.