Research Thesis Topic
Energy Demand and Price Forecasting with Artificial Intelligence Models for Consumer Energy Predictability
Accurate and reliable predictive models for short and long-term electricity demand and energy price are critical in engineering and science applications as these models can assist in decisions made by renewable and conventional energy engineers, electricity providers, end-users, and government entities to address energy sustainability challenges to support National Electricity Market (NEM). The energy demand knowledge also assists in the expansion of energy distribution networks, energy pricing, and energy policy development. The energy price and demand are often inter-related and covary so a model representing their changes is essential to model future demand and price.
In this research project, students will design artificial intelligence models to forecast energy load and energy price over minute-scale, hourly, daily or seasonal scales. They will build joint distribution (i.e. multivariate) models based on copula and deep learning for a knowledge-based expert system. As a major contribution to energy modelling research, students will develop models based on other significant predictors, particularly in geographically diverse locations where climatic factors affect the electricity load. The study will therefore use climatic factors (e.g. temperature) in the prediction of electrical energy demand and energy pricing. The project will suit students with background in electrical engineering, mathematics, computing and renewable energy. Students will explore and apply deep learning models for improved design of data-intelligent models.
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.
- Institute for Agriculture and the Environment
- International Centre for Applied Climate Sciences
- School of Agricultural, Computational and Environmental Sciences
- Artificial Intelligence and Image Processing
- Atmospheric Sciences
- Computer Software
- Electrical and Electronic Engineering
- Information Systems
- Mathematical Physics
- Numerical and Computational Mathematics
- Other Engineering
- Other Information and Computing Sciences
Please review the admission requirements for the academic program associated with this Thesis Topic
The project will suit students with background in electrical engineering, mathematics, computing and renewable energy. Students will explore and apply deep learning models for improved design of data-intelligent models. The project is scalable for further research, such as to a Masters or a PhD program to advance research careers, acquire programming skills, modelling and publication.