RISE Discoveries; Decarbonising retrofit buildings with data, Insights from Atamate

Data driven approach to building performance

Atamate are a data-led building specialist based in the United Kingdom (UK). The emphasis is on collecting and using operational data to shift building management from assumption-driven decision making to evidence-led control. In practice, this data layer is framed as enabling tighter building control, faster alerts and notifications, and a clearer view of real performance through digital twin style insights.

Rapid, responsive control is presented as a route to the biggest gains, rather than relying solely on upgraded equipment. Ventilation is highlighted as another area where richer datasets, including air-quality signals, can enable smarter control and potentially support alternative mechanical approaches that better match demand.

Case study with Oxford Brookes University

This study conducted by Kat Kelly (Atamate), P. Sassi (Oxford Brookes University) and Joe Miles (Atamate) investigated the results of how the gathering of data from an automated control system can lead to very low energy consumption. The study looked at six neighbouring, modern-built flats in Cardiff, Wales.

Some key results of the case study were:

• Energy Savings vs Standard Assessment Procedure Estimates: Actual energy consumption was 34% lower in one building and 12% lower in the other compared to SAP predictions.
• Upper-floor flats achieved 25%–72% better performance than SAP estimates. Ground-floor flats performed worse than expected (possible design issues like bay windows and cellars).
• Performance vs Passivhaus Standard: Mid and top-floor flats outperformed Passivhaus threshold (15 kWh/m²/year).
• Ground-floor flats were close to Passivhaus despite not being designed to its strict standards.
• Dynamic Simulation Model (DSM): Actual consumption was comparable to or better than MVHR-based designs in some cases.

A key takeaway from this case study, is that data is the foundation of automated heating and ventilation systems. The data gathered here transforms static heating systems into intelligent and responsive solutions which drives both sustainability and cost savings.

The full academic paper can be viewed here, and makes for another interesting read!

Overcoming policy barriers for data-led retrofit projects

One of the key policies in the UK is Part L of the UK Building Regulations. It focuses on conservation of fuel and power and helps to ensure buildings are energy efficient and sustainable. It splits buildings into domestic and non-domestic.

Non‑Domestic Buildings

For non‑domestic projects, compliance is typically modelled using SBEM (Simplified Building Energy Model), which is described as more flexible. Designers can often gain “credit” for controls and operational strategies, making it easier to justify improvements that rely on how systems are managed in practice.

Domestic Buildings

Domestic compliance under Part L1 currently uses SAP and RdSAP (Reduced SAP). This is due to change with the introduction of the Home Energy Model (HEM). While there is an innovation route (Appendix Q) intended to allow new technologies into SAP, it is sometimes difficult to navigate—creating a barrier to adoption. This is highlighted as a wider problem for heat decarbonisation, where innovation is needed but can be blocked by the current regulatory regime requirements. Changes are expected because of the introduction of the HEM.

Why Data Matters for Retrofit

For housing stock owners, the notes recommend a data-led retrofit approach:

• Know and segment your stock—prioritise by building typology (age/fabric/performance) and/or occupant group. Use existing data such as SAP records and any occupancy studies.
• Establish a baseline—ideally collect a year of “as-is” performance data. At minimum: occupancy, indoor temperature, and air quality; ideally also measure mechanical system performance.
• Design evidence-based interventions—stay technology-agnostic and choose what best fits the building. Examples include exhaust air heat pumps, MEV[NM2]  (Mechanically Exhausted Ventilation)(especially demand-controlled ventilation), and potentially adapting commercial-sector solutions (e.g., low-cost, high-performance through-the-wall air-to-air heat pumps) for domestic use.
• Verify, refine, and scale. Compare pre/post retrofit data to confirm performance, tweak for greater efficiency, then roll out proven approaches across similar properties. With ongoing monitoring and controls, buildings can be continuously optimised over time.

Simplifying controls and the future of data in buildings

Commercial buildings are positioned as offering an immediate opportunity for impact through a “Quick Win” stage. Once monitoring sensors are installed, control can be taken over key mechanical systems straight away. The implication is that efficiency gains don’t have to wait for major refurbishments. Basic automation can quickly cut waste by stopping heating in empty spaces, modulating ventilation based on air quality, and avoiding needless heating when windows are opened. The broader ambition is for this layer of optimisation to operate quietly in the background, reducing the need for constant human intervention.

Residential settings, meanwhile, are framed less around instant system takeover and more around the importance of simple, intuitive user control. A streamlined approach to temperature management, such as a configurable “fallback” point that adapts to conditions can reduce complexity without removing comfort.

A single “Boost” function illustrates this, as occupants can temporarily raise warmth when needed, while automation still prevents waste by cutting heating when spaces aren’t being used. The underlying fact is that simplifying controls isn’t just a user-experience improvement but is often presented as a practical route to better energy performance and reduced consumption.

What is next and what does the future look like with data use in retrofit?

However, progress is framed as being limited by the UK’s prescriptive regulatory approach, which can prioritise compliance with specified. A contrasting model used elsewhere in Europe focuses more on performance-based targets, such as energy consumption measured in kWh per square metre per year. While not presented as a perfect solution, this outcome-led framework is positioned as a more effective driver for genuine efficiency improvements, and a way to give innovators the freedom to raise building performance.

A key takeaway

Cost is often a useful proxy for carbon. If a solution is expensive, it can indicate higher energy and resource intensity, and it is therefore less likely to be the most environmentally sound option.

Want to learn more?

Listen to the podcast on Rise’s Spotify channel:

Decarbonising Retrofit Buildings with Data: Insights from Atamate