The importance of understanding heating and environmental conditions in student accommodation has grown significantly. Rising energy costs, increasing awareness of sustainability, and the need to ensure student wellbeing, mean providers are turning to data-driven solutions to optimise their living spaces. The challenge lies not in collecting data—modern sensors and smart devices do this continuously—but in extracting meaningful, actionable insights from that data.
Student rooms generate a vast array of data points. Temperature, humidity, sound pressure, CO₂ levels, occupancy patterns, and heating system performance can all be tracked. These data streams provide a real-time snapshot, helping providers ensure comfort, promote energy efficiency, and identify potential maintenance issues before they escalate.
However, raw data is rarely immediately useful. For example, a temperature sensor might report fluctuations throughout the day, but without context, it’s unclear whether those changes indicate a problem, or are just normal daily cycles. That’s where data analysis and contextualisation come in.
Sensors can produce noisy or incomplete data—perhaps due to connectivity issues or equipment faults. A system such as Irus draws on vast datasets and aligns information from different sources, such as correlating temperature readings with timestamps and room occupancy to provide sensible insights.
Identifying Patterns
The data is analysed to identify trends and anomalies. Time-series analysis helps detect patterns over days, weeks, or even seasons. For instance, if a particular room consistently shows lower temperatures than the rest of the building, it may indicate poor insulation or a malfunctioning heat source. Alternatively, if occupants frequently open windows in winter, it might point to overheating or poor ventilation.
Clustering groups of rooms with similar environmental characteristics, helps facilities teams prioritise maintenance. And unusual behaviour can be flagged—like higher room temperatures than the system is set to—signifying the use of supplementary heaters.
Combining Environmental Data with Behavioural Insights
To extract meaningful information, environmental data should be combined with behavioural and usage data. Intelligent thermostats with multi-sensors offer a fuller picture. For example, linking low room temperatures with room absence, this can help differentiate between a technical issue and an intentional energy-saving decision.
Additionally, integrating data regarding student comfort can ground quantitative findings in real-world experience. If multiple residents report discomfort in certain rooms, data analysis helps pinpoint the root cause and validate the claims with hard evidence.
Practical Applications and Outcomes
With the right analysis, operators of buildings can achieve significant outcomes:
- Energy Efficiency: Identifying overheating zones and optimising heating profiles can reduce energy consumption and costs.
- Improved Comfort: Monitoring CO₂ levels, ventilation quality, and humidity ensures students have a healthy indoor environment, which is essential for concentration and wellbeing.
- Preventive Maintenance: Detecting irregularities in heating systems early, and pinpointing the exact location of issues, allows for proactive maintenance, reducing downtime (and search time) and costly emergency repairs.
- Informed Planning: Long-term data trends can inform renovations, retrofits, and even the design of new buildings to meet sustainability goals. Not to mention the procurement of utilities.
Irus benchmarking
Software tools within the Irus ecosystem make all this possible. With more than 75,000 Controls across 150 sites the dataset is of a significant magnitude to enable geographical or building type/age benchmarking for your property. This will return meaningful insights and recommendations for optimising both energy and operational efficiency.
This is a real step towards smarter, more sustainable, and student-centred environments. By turning raw sensor data into actionable insights, providers are making evidence-based decisions that improve the student experience and their own efficiencies. The key is in connecting the dots: contextualising, analysing, and acting on the data with a clear purpose.
This article appeared in the May 2025 issue of Energy Manager magazine. Subscribe here.