Zhifeng Guo

Qualifications

• MSc Management Science and Engineering, Hefei University of Technology(2019)
• BSc Business Administration, Hefei University of Technology (2015)    

Scholarship

• Vice Chancellor's Research Scholarships (October 2019 - September 2022)  

A machine learning based framework for exploring drivers of residential electricity consumption patterns and forecasting demand.

Electricity plays an essential role in supporting human wellbeing and economic growth. However, the main way of generating electricity worldwide is through burning of fossil fuels, which is a major contributor to environmental pollution and global warming. Gaining a better
understand of household electricity consumption patterns as well as behaviours and household characteristics that influence consumption, would prove enormously useful to policy makers and power companies in developing effective strategies aimed at reducing residential electricity consumption. The recent advances in big data and machine learning come at an ideal time to help address this challenge. It is now possible, for example, to digitally store massive amounts of electricity usage data. Advanced machine learning methods, in turn, provide an ideal tool for analysing these large datasets, opening up new avenues to explore electricity consumption patterns in much greater depth.

• Professor Jesse O'Hanley
• Dr Stuart Gibson 

1st Supervisor's Research Group

Management Science