FindAPhD Weekly PhD Newsletter | JOIN NOW FindAPhD Weekly PhD Newsletter | JOIN NOW

Data science in energy efficiency and intelligent management in built environment


   School of Computing, Engineering & the Built Environment

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr I Wong, Dr Leo Chen Yi  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Reference number: SCEBE/21SF/014/IW

Background

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.

Specification

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


Funding Notes

Applicants are expected to find external funding sources to cover the tuition fees and living expenses. Alumni and International students new to GCU who are self-funding are eligible for fee discounts.
See more on fees and funding. https://www.gcu.ac.uk/research/postgraduateresearchstudy/feesandfunding/

References

For further information, please contact:
Director of Studies
Name: Dr Ing Liang Wong
Email: ingliang.wong@gcu.ac.uk
GCU Research Online URL: https://researchonline.gcu.ac.uk/en/persons/ing-liang-wong
2nd Supervisor
Name: Dr Leo Chen Yi, University of Newcastle
Email: leo.chen@ncl.ac.uk
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs