Wind power is a promising renewable energy. Wind industry is the fastest growing renewable resource and is expected to continue to grow over 2030s although the production of real energy will rely on accurate simulation models of wind speed over hourly, daily and monthly periods. Wind prediction models can enable short-term (real-time) and long-term wind energy feasibility studies and future wind power investments.

Australia and many parts of the world has excellent wind resources. Although research in onshore and offshore wind farms must be performed, reliable wind prediction models that can inform future sustainability of wind power are still lacking. Wind energy resources in potential wind farm sites require an integrated high-quality monitoring system forecasting the micro-scale model of wind flow incorporating the effects of topography and terrain. However, forecast models that provide reliable information on wind power sustainability, and address stochastic behaviour of wind regimes for more accurate predictions, can assist in economically-viable future investments, to solve wind energy utilization challenges.

In this project, students will learn about the design of farms and optimisation of power using high-precision forecasting tools and geophysical, statistical and evolutionary methods. It will consider uncertainty and power-failure risks, effect of wakes with power production, atmospheric stability on performance and loading characteristics throughout a typical daily cycle, power production in extreme event and optimum placement of wind system. Students will apply machine learning (i.e., artificial intelligence) to predict wind speeds at topographical and geographic locations. Machine learning is unique as a fast and efficient data transformative tool, yet the applications in renewable energy remain very limited. The project will suit students in renewable energy, engineering, computing, climate, meteorology, mathematics, statistics, environmental science and atmospheric physics. Students have opportunity to engage constructively with supervisor to publish and further research.

This project is suitable for PhD, Research or Coursework Masters Thesis. It provides opportunity to publish in high quality Q1 journals. The research student will be part of the Advanced Data Analytics Research Group under Prof Ravinesh Deo.

For more details see https://eportfolio.usq.edu.au/view/view.php?id=116719.

Professor Ravinesh Deo

• Assoc Prof Nathan Downs
• Dr Nawin Raj

• Institute for Agriculture and the Environment
• International Centre for Applied Climate Sciences
• School of Agricultural and Environmental Sciences

• Artificial Intelligence and Image Processing
• Atmospheric Sciences
• Mechanical Engineering

• Doctor of Philosophy (DPHD)
• Doctor of Philosophy (DPHD)
• Master of Research (MRES)
• Master of Research (MRES)