Alex Amies is a postgraduate doctoral researcher in the Department of Engineering. His research falls within the shared domain of systems engineering and applied data science, with particular focus on the informatic interrogation of BAS/BMS metadata associated with HVAC control in commercial properties. He was awarded an IMechE undergraduate scholarship to read Mechanical Engineering (MEng) at the University of Bristol (graduated 2004). This was followed by a second master’s in Building Services Engineering (MSc) at LSBU (graduated 2016).

Now on his sixth start-up venture, Alex has 21 years’ entrepreneurial experience with 10 years in parallel as a commercial employee. He is a Chartered Engineer with a CIBSE Fellowship and IMechE Membership. His career spans controls engineering, building automation, dynamic energy simulation modelling and analytics.

Alex co-founded re:sustain in 2021, a PropTech start-up focused on the remote optimisation of commercial real estate. As Chief Scientific Officer, he helped maximise operational energy efficiency, achieving up to 42% savings with measured accuracy levels of 96% aligned with ASHRAE Guideline 14.

In 2025, Alex co-founded Building Informatics R&D Lab Ltd, the official industry sponsor for his PhD. His research formalises applied R&D from industry practice, with relevance to automated onboarding, thermovolumetric optimisation and scalable parametric calibration of physics-based simulation models.

Research Interests

• Building automation/management systems
• HVAC controls analysis
• Building analytics and metadata interrogation
• Thermovolumetric optimisation
• Dynamic energy simulation modelling

Thesis Title

Informatic system interrogation to enhance HVAC automation and optimisation for commercial buildings

Abstract: 

This research undertaking falls within the shared domain of systems engineering and applied data science, with focus on the informatic interrogation of Building Automation/Management System (BAS/BMS) metadata associated with Heating, Ventilation and Air-Conditioning (HVAC) control in commercial properties. The core aim is to support a step change in the scalability of systems optimisation throughout the real estate sector by enabling scalable, cost-effective, system-agnostic deployment across numerous building portfolios.

To address this aim, a reliable method is to be developed for automatic identification, definition and verification of system components and control points directly from raw BAS/BMS backup files without dependence on labels/names manually entered by engineers, for example. A five-part ontological framework (consisting of a combination of distinct approaches: categorical, logical, numerical, nominal, and consequential) is proposed for deep interpretation of structural metadata (referred to here as interrogation).

Beyond the onboarding phase, these methods can naturally lead to thermovolumetric optimisation with real-time occupancy tracking and eventually to partial automation of the calibration of dynamic energy simulation models (also known as digital twins). The former would involve interpretation of CO₂ sensor data representing conditions in all of the return air ducts of ventilation systems, enabling dynamic control of volume flow rates based on inferred building population. The latter would involve using iterative processes and parametric analysis to partially automate queued and parallel calibration procedures to match models to actual building energy use.

Supervisor Team

First Supervisor: Dr Wei He

Second Supervisor: Professor Raúl Rosales