Artificial intelligence to Building Energy Systems for Next Generation Control Strategies

HVAC accounts for 60-70% of all end user energy in buildings. Despite advanced in

solar/thermal technologies there has been no real reduction in consumption across

the sector. Multiple factors influence the demand and deployment to maintain

thermal comfort and meet regulatory requirements.

This project will capitalise on the emergence of AI tools to develop a novel control

system based on user requirements to optimise and regulate the usage of HVAC

across a range of commercial buildings.

The project will use desk based modelling across range of current live projects with

the opportunity to design and test in physical buildings to validate the work whilst

adding a valuable contribution to the existing research outputs and team.

The successful candidate will develop, apply and validate AI-based models (based

on machine learning, agent-based, etc) primarily to:

i. predict energy demand in multi-energy systems (airflow, electricity, heat)

ii. dynamically manage building systems to maintain thermal comfort

iii. discovery & optimize new designs of future Building Management Systems

(BMS).

This PhD project will cooperate with national and international projects and you will

contribute to develop real-life multi-energy energy systems by applying, training and

validating models to systems in the UK and Africa.

Requirements:

Applicants should have a first-class degree or good 2:1 (or equivalent) in Energy

Engineering, Mechanical Engineering, Electrical Engineering, Computer Science or a

related field, ideally with prior experience in building energy systems. The ideal

applicant should possess relevant analytical skills, proficiency in programming, and

proficiency in modelling in high-level programming environment (e.g. Python, Matlab,

Ansys CFD etc).

Deadline:

The position will be filled as soon as a suitable person has been found; hence you

are encouraged to apply as soon as possible. PhD Starting September 2024 or soon

after.

Supervision Team & Further Info:

Supervisors: Prof. Ben Hughes; School of Mechanical Engineering and the Energy

Institute

For enquires and to apply please contact Prof Ben Hughes

at [email protected] and attach your CV