Heating and Environmental Data: Key Benchmarking Insights

Hello, Champions of Net Zero!

The landscape of student accommodation is undergoing a transformative shift, driven by the increasing necessity to comprehend heating and environmental conditions. With energy costs on the rise, a growing awareness of sustainability issues, and a heightened focus on student wellbeing, accommodation providers are increasingly turning to data-driven solutions to enhance their living spaces. The challenge they face, however, is not simply in gathering data—thanks to modern sensors and smart devices that operate continuously—but rather in extracting meaningful, actionable insights from this wealth of information.

Every student room generates an impressive array of data points. These include temperature, humidity, sound pressure, CO₂ levels, occupancy patterns, and the performance of heating systems. Collectively, these data streams offer a real-time snapshot of the environment, empowering providers to ensure comfort, promote energy efficiency, and identify potential maintenance issues before they escalate into larger problems.

Yet, raw data by itself is seldom immediately useful. For example, a temperature sensor may report fluctuations throughout the day, but without proper context, it remains unclear whether these changes signify an underlying issue or are merely part of normal daily cycles. This is where the importance of data analysis and contextualisation comes into play.

It’s crucial to acknowledge that sensors can produce noisy or incomplete data, potentially due to connectivity issues or equipment malfunctions. Here, systems like Irus come into play, drawing from extensive datasets and aligning information from various sources. For instance, by correlating temperature readings with timestamps and room occupancy, Irus can provide sensible, actionable insights.

Identifying Patterns in Student Accommodation

The analysis of data allows us to identify trends and anomalies effectively. Techniques such as time-series analysis are particularly useful in detecting patterns over days, weeks, or even months. For example, if a specific room consistently displays lower temperatures than the rest of the building, this could indicate insufficient insulation or a malfunctioning heat source. Conversely, if residents frequently open windows during the winter months, it might suggest issues of overheating or inadequate ventilation.

Furthermore, clustering groups of rooms with similar environmental characteristics enables facilities teams to prioritise maintenance tasks efficiently. Unusual behaviours can also be flagged; for instance, if room temperatures exceed the set parameters significantly, this may indicate the use of supplementary heating devices.

Combining Environmental Data with Behavioural Insights

To derive meaningful information, it is essential to combine environmental data with behavioural and usage patterns. Intelligent thermostats equipped with multiple sensors can provide a more comprehensive picture. For instance, by linking low room temperatures with periods of room absence, accommodation managers can differentiate between a technical issue and an intentional decision to conserve energy.

Moreover, integrating data concerning student comfort can ground quantitative findings in real-world experiences. If multiple residents report discomfort in specific rooms, data analysis can help identify the root causes and validate these claims with robust evidence.

Practical Applications and Outcomes for Student Accommodation

When the right analytical tools are employed, operators of student accommodation can achieve significant outcomes that benefit both the students and the providers:

• Energy Efficiency: By identifying overheating zones and optimising heating profiles, providers can significantly reduce energy consumption and related costs.
• Improved Comfort: Continuous monitoring of CO₂ levels, ventilation quality, and humidity ensures that students inhabit a healthy indoor environment, which is crucial for concentration and overall wellbeing.
• Preventive Maintenance: Early detection of irregularities in heating systems, along with precise identification of issues, allows for proactive maintenance. This approach can minimise downtime and reduce the need for costly emergency repairs.
• Informed Planning: Long-term data trends can guide renovations, retrofits, and even the design of new buildings, aligning with sustainability goals and optimising utility procurement.

Irus Benchmarking

The software tools within the Irus ecosystem make all of these advancements possible. With over 75,000 controls across 150 sites, the dataset is substantial enough to enable geographical or building type/age benchmarking for properties. This benchmarking returns meaningful insights and recommendations for optimising both energy and operational efficiency.

This marks a significant step towards creating smarter, more sustainable, and student-centred environments. By transforming raw sensor data into actionable insights, accommodation providers are equipped to make evidence-based decisions that enhance the student experience while simultaneously improving their operational efficiencies. The key lies in connecting the dots—contextualising, analysing, and acting on the data with a clear purpose.

In conclusion, as student accommodation providers embrace these innovative data-driven solutions, they are not only contributing to the wellbeing of students but also championing sustainability in the built environment. The future of student living is not only about providing shelter; it’s about creating a thriving ecosystem that prioritises energy efficiency, comfort, and proactive maintenance, paving the way for a greener tomorrow.

For further insights and information, feel free to visit www.prefectcontrols.com.

This article originally appeared in the May 2025 issue of Energy Manager magazine. Subscribe here for more in-depth articles and updates on energy management and sustainability.