Data-Knowledge Integrated AI Decision Framework for Optimizing IoT Connected Building Assets

Summary

Climate change necessitates improved energy efficiency. Refrigeration in the retail sector and in building air conditioning systems is a major energy consumer. How can we optimise energy use while protecting food products and maintaining the health and well-being of building occupants?

The Internet of Things (IoT) has transformed how refrigeration systems are managed. They generate continuous telemetry data, providing detailed information on performance. Predictive assets performance analysis and their optimisation is essential in driving the efficiencies required. This, however, is complex due to necessary trade-offs between optimisation factors and involves multi-disciplinary research.

Traditional optimisation of IoT assets focusses on energy efficiency, but constraints critical to the asset's primary function either prevent full optimisation or overlook them. Physical models are typically static and do not represent the complete physics of the system. They are nevertheless deemed trustworthy since we know which physical processes are represented in the model and can limit their numerical errors. Most AI-based data-driven models are black-box models without a physics connection, making them unreliable for safety-critical applications. Domain knowledge is essential throughout data analysis, especially for explainability.

The aim of this PhD research is to develop an advanced data-knowledge integrated AI decision framework that integrates an advanced data-driven modelling framework based on all data inputs inclusive of telemetry, weather, meta, and future sources such as video with the domain knowledge, including the physical models of the asset operation, expert experience, as well as the policy and regulations, to optimise IoT connected building assets for energy efficiency and other non-energy related critical factors simultaneously in real time. This multidisciplinary integration may alleviate the crucial weakness of each discipline individually. The timeliness of this PhD project becomes also apparent in the potential of the above integration to contribute to the long-standing sustainability goal.

The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promoting and advancing gender equality in Higher Education. We particularly welcome female applicants, as they are under-represented within the School

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• DfE CDP Award in collaboration with LoweConex

This CDP PhD studentship offers an annual non-taxable maintenance grant of approx. £19,500 (plus an additional annual stipend top-up of £1500). The grant covers three years of tuition fees (worth over £14,000) and provides extensive support for research training and project running costs.

To be eligible for this scholarships, applicants must meet the following criteria:

• Be a UK National, or

• Have settled status, or

• Have pre-settled status, or

• Have indefinite leave to remain or enter, or

• be an Irish National

Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.