Reference number: SCEBE/21SF/014/IW
The energy consumption in the building sector has increased steadily due to the use of HVAC systems. As part of energy benchmarking process, the quality and quantity of energy data exploited are important in achieving energy efficiency built environment. With evolve in data science techniques and intelligent energy management, such as control and automation, smart metering, and real-time monitoring, sparse data in new and diverse forms can be exploited by artificial intelligence or machine learning techniques. The use of data science techniques can increase the energy efficiency in the built environment by accurately monitor, collect and store the huge amount of data.
Aim and scope of work
This project aims to develop data-centric energy models for accurate simulations of building energy performance and building energy management. Data science will be used to address the following challenges in the area of building energy management: prediction of energy demand, analysis of building operations, detection of energy consumption patterns and AI & machine learning.
The successful applicant will be from building physics or computing engineering background holding the minimum of a first degree (2:1 or above). Good understanding and prior experience on data science techniques that applied to building energy modelling will be an advantage.
Candidates are requested to submit a more detailed research proposal (of a maximum of 2000 words) on the project area as part of their application.
How to apply
To apply, please use the relevant links below:
· As a full-time student: https://evision.prod.gcu.tribalsits.com/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=D27BLTENVFT&code2=0006
· As a part-time student: https://evision.prod.gcu.tribalsits.com/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=D27BLTENVPT&code2=0006