QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.

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Found 30 matching student topics

Displaying 25–30 of 30 results

Internet of Mobile Energy

The emergence of the two-way communication model and Distributed Energy Sources (DES) is transforming traditional power systems from largely centralised energy production to more decentralised and connected management systems. This is called the 'smart grid'.As the smart grid evolves, electric vehicles (EVs) are emerging as unconventional and highly-disruptive participants in the grid that can add significant benefit and flexibility. Notably, EVs are equipped with a relatively high capacity battery that stores energy to power the vehicle.EV batteries, coupled with the …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Computer Science

Metal polymer batteries and supercapacitors for renewable energy storage

Australia boasts rich wind and solar energy resources. To avoid fluctuations placing severe burden on the power grids, a reliable and efficient battery storage is required.The present technology based on lithium-ion batteries suffers from high manufacturing costs, poor safety and short life-span. Metal-polymer batteries are expected to overcome the storage and the charging speed of the traditional batteries in the near future, opening new avenues for renewable energy resources …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Chemistry and Physics
Research centre(s)
Centre for Materials Science
Centre for Clean Energy Technologies and Practices

Physics informed machine learning for energy forecasting

Accurate forecasting is at the heart of many modern industries from energy and transport to retail, supply chains, finance, climate, and health. This research project explores deep learning approaches for time-series forecasting, investigating how modern architectures such as recurrent neural networks, LSTMs, Temporal Convolutional Networks, transformers, and multimodal foundation models can shape the next generation of forecasting systems.The overarching goal is to develop robust, interpretable, and scalable forecasting models that outperform classical methods and work effectively in real-world settings, including …

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Energy Transition Centre

Decision optimisation in energy supply chain

this project aims to develop integrated forecasting and decision optimisation models for renewable energies.

Study level
PhD
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

Probabilistic forecasting of energy

This project aims to develop probabilistic forecasting models for renewable energies vi a Bayesian approach.  The models will be developed for very short term and short-term (10 minutes to 24 hours ahead).

Study level
PhD
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

Spatio-Temporal Forecasting of renewable energies

This project aims to develop short-term (up to 24 hours ahead) forecasting models that take into account the spatial as well as temporal information in wind farms and solar farms. Such models are useful for operational planning in farms and stabilising the network.

Study level
PhD, Master of Philosophy, Honours
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

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