PREDICTOR: Improving the energy performance of closed-loop heating and cooling systems.

The research hypothesis is that decision support tools enabling optimum maintenance interventions in closed loop HVAC systems, based on life cycle cost, can deliver significant energy reduction in buildings. The aim of this project is to develop decision support tools to predict when timely maintenance intervention can cost-effectively optimize the operation and maintenance of closed loop HVAC systems in buildings. We aim to test the hypothesis by achieving the following five objectives:
1) Investigate the life cycle performance of different closed loop HVAC systems by analysing commissioning and maintenance data against designed energy performance.
2) Develop data driven HVAC performance models, using machine learning techniques linked to 'live' sensor data, to advise the decision support tool regarding system operational efficiency
3) Develop a decision support tool for HVAC systems (based on a commissioning and operational data, and innovative lifecycle cost benefit analysis), linked to a Building Energy Management framework, enabling real-time intervention to optimise maintenance and energy efficiency.
4) Test and evaluate a prototype of the proposed decision support system for energy management in non-domestic buildings, to enable adoption across the sector and to exploit commercialisation opportunities with industry.
5) Disseminate the results, develop a simple, interactive educational tool and create standards recommendations and guidelines for design, commissioning and maintenance of HVAC systems.
PhD timetable:
Year 1: Setting up an experimental rig and relating energy consumption to maintenance and design data
Year 2: Developing HVAC performance using data driven models ANN and life cycle cost
Year 3: Evaluate and test prototype system and develop interactive educational tools, writing up and submitting two journal papers