Projects
My research is focused on industry-oriented problems. I have worked closely with several industries during my career at university. This includes projects on developing a predictive model for wind farms, decision-making tools for aviation industries, developing a sales forecasting model for food and beverage companies, developing a predictive and prescriptive model for water treatment companies, among others. Please do not hesitate to contact me if you need my expertise in forecasting at your presentations.
Selected projects
Virtual Power Plant: Forecasting extreme events in electricity price, CSR Co, Australia
In this project, we developed and implemented a machine learning model to predict extreme events of electricity price when energy can be expensive. We proposed a machine learning model to predict the electricity price for the next 24 hours and then used it in a mixed integer linear programming model to charge and discharge batteries and thus save on operational costs
Digital Enablement in Waste Water Treatment, Urban Utility, Brisbane:
In this project, we developed two machine learning models to predict the power flow to the wastewater treatment site, and optimise the operations with a mixed integer linear model. We then developed an ML model to predict the solutions of the ML model in real-time. The models are implemented and tested on real-world data from Urban Utility.
Forecasting renewable energies: Power generation from wind and solar farms, Worley Co Aug. 2020 and Australian Energy Market Operator (AEMO), Australia.
Project Summary: In this project, we developed and implemented a machine learning model that can predict the power in wind farm for the very short term in Waterloo Wind farm. The model was tested with real-world data under live data for 8 weeks and outperformed the AEMO model by 47%. See Media release.