Skip to main content
  • Home
  • Thesis Topics 
  • Energy Demand and Price Forecasting with Data Intelligent Models for Consumer Energy Predictability

Research Thesis Topic

Energy Demand and Price Forecasting with Data Intelligent Models for Consumer Energy Predictability


Topic ID
324

Thesis Topic/Title
Energy Demand and Price Forecasting with Data Intelligent Models for Consumer Energy Predictability

Description

Predictive models for short and long-term electricity energy demand and energy price are critical in engineering and science applications as they can assist renewable and conventional energy engineers, electricity providers, end-users, and government entities in addressing energy sustainability challenges for National Electricity Market (NEM). Demand knowledge also assists in the expansion of distribution networks, energy pricing, and energy policy development.

In this project, the students will design intelligent models to forecast energy loads and energy price over minute-scale, hourly, daily and seasonal scales. Students will apply Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Support Vector Regression (SVR), genetic algorithms, fuzzy logic, knowledge-based expert systems, and Multivariate Adaptive Regression Splines (MARS) among several popular forecasting tools used by energy researchers. As a major contribution to energy 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. 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.


Principal Supervisor


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

Field of Research
  • Applied Mathematics
  • Computer Software
  • Electrical and Electronic Engineering
  • Information Systems
  • Mathematical Physics
  • Numerical and Computational Mathematics
  • Other Engineering
  • Other Information and Computing Sciences


Application Open Date
11/02/2018

Application Close Date
31/12/2019

USQ Scholarship Applications

Other Scholarship Funding Details
Australia Awards (Country-Specific Closing Dates), and Australian Endeavor Scholarships (Close June 2018)

Pre-approved for Ethics
Not Applicable

Admission Requirements

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.




Back to List