How can operational data help close the gap between designed performance and real occupant experience? The text explains how integrating energy and IEQ data is essential for human-centred buildings.
Loes Visser, Managing Director at DataBuilt | LinkedIn profile
Sarvin Sarabi Daryani, Business developer of Sensi – label at DataBuilt | LinkedIn profile
(Note: Opinions in the articles are of the authors only and do not necessarily reflect the opinion of the European Union)
Across Europe, ambitions for buildings are increasing rapidly. Energy targets are becoming stricter, digitalisation is accelerating, and with the recast Energy Performance of Buildings Directive (EPBD), indoor environmental quality, health and wellbeing are finally being treated as part of building performance, not as afterthoughts.
On paper, that sounds like genuine progress. In practice, many so-called ‘smart’ buildings still struggle to deliver both low energy use and good indoor conditions once people move in. That is not because the technology is missing. Most buildings today are full of sensors, systems and data. The problem is more fundamental: energy performance and occupant experience are still treated as separate worlds.
Energy performance is typically assessed through calculations, models and aggregated consumption figures. Indoor environmental quality only becomes visible when something goes wrong, through complaints, surveys or one-off measurements. By the time those signals appear, the building is already in use, settings are fixed, and adjustments are made reactively. What gets lost in this split approach is how buildings actually function day to day. Real buildings are shaped by occupancy, control strategies, seasonal effects, and human behaviour. Energy use, system behaviour and comfort are not separate outcomes; they are different expressions of the same underlying reality. If we want buildings to be genuinely human-centred and not just comply with regulations, we need to start there.
A recurring theme in building practice is the discrepancy between calculated performance and operational reality. Energy models, compliance calculations and design assumptions are usually based on normative usage profiles, fixed occupancy schedules and simplified control strategies. These methods remain useful for regulatory purposes, but they struggle to represent how buildings are used once occupied.
Interviews with building professionals consistently point to the same issue: once a building is handed over, it enters a phase where assumptions quickly unravel. Occupancy densities differ from the design brief, spaces are repurposed, users intervene and adjust systems to their preferences and control strategies are modified incrementally to resolve complaints rather than optimised holistically. When these adjustments accumulate, the building in operation may have little resemblance to the building that was simulated.
The consequences go beyond energy. It directly affects indoor environmental quality. Temperature stability, air quality, acoustic comfort and lighting conditions all depend on how systems interact with real occupancy patterns and operational decisions. When these interactions are poorly understood, the result is often a trade-off: energy savings achieved at the expense of comfort or comfort maintained through energy-intensive overrides.
What is often missing is occupant patterns as a continuous input rather than a reaction to escalating problems. By the time a complaint reaches a facility manager, discomfort has already accumulated. A human-centred approach sees comfort as an outcome to actively maintain, not as a threshold to avoid breaching.
From a human-centred perspective, this is a fundamental weakness. Buildings are designed for people, yet performance evaluation rarely starts from how people experience the building over time.
Figure 1: Performance gap in a building life cycle. Assumed performance stays constant from design while actual building performance diverges from the moment of occupation.
The last decade has seen rapid growth in building automation, monitoring platforms and sensor networks. Many buildings can now be classified as ‘smart’ in a technical sense. However, intelligence is often defined by the amount of data collected rather than by how effectively that data informs decisions.
In practice, operational data is frequently fragmented across systems. Energy data is analysed separately from indoor climate data; fault detection focuses on equipment performance rather than user impact; dashboards present trends without context. Several interviews highlight that facility managers are often overwhelmed by information but lack actionable insight.
Another issue is access to data. Energy data can be accessed through one party, building management system data is managed by another, and comfort data (if existing) with a third party. Occupants experience the combined effect of all three but rarely are all three datasets accessible simultaneously. Besides data integration, human-centred buildings require organisational integration.
A human-centred building requires a different starting point. Instead of asking whether systems operate according to specification, the key question becomes whether the building consistently supports health, comfort and functional use within acceptable energy and carbon boundaries. This requires linking energy flows, system behaviour and IEQ indicators at the level where occupants experience them: zones, time periods and usage patterns.
Figure 2. Current practice versus a human-centred approach to building data. When energy and IEQ data are managed separately, decisions remain reactive. Integrating different data into a single operational view enables early detection and proactive decision-making.
The recast EPBD explicitly recognises IEQ as part of building performance. This represents an important shift from static design criteria to operational outcomes. However, translating this ambition into practice remains challenging.
Traditional IEQ assessments are often episodic. Measurements are carried out during commissioning or in response to complaints but are rarely embedded in continuous performance management. As a result, trends remain invisible. Seasonal effects, slow degradation of system settings or the cumulative impact of small adjustments go unnoticed until problems escalate.
Interview material repeatedly emphasises that many comfort complaints could be prevented if early signals were recognised and interpreted correctly. For example, rising temperature variability in specific zones may indicate mismatched control strategies long before occupants report discomfort. Similarly, declining ventilation effectiveness can often be detected through combined CO₂, occupancy and energy data rather than through complaints alone.
Treating IEQ parameters as operational performance indicators, rather than exceptions, allows buildings to be managed proactively. This is a prerequisite for human-centred operation.
Energy use and indoor climate are often managed by different disciplines, using different tools and success criteria. This separation is deeply embedded in organisational structures, contracts and software ecosystems. Yet, from a physical perspective, energy and IEQ are inseparable.
Heating, cooling and ventilation energy is consumed precisely to create indoor conditions. When these conditions are analysed independently of the energy required to achieve them, optimisation becomes one-sided. Interviews with engineers and advisors highlight that many apparent energy inefficiencies only become understandable when viewed in relation to comfort demands, occupancy patterns and control logic.
An integrated approach focuses on relationships rather than isolated metrics:
Answering these questions requires time series data, contextual knowledge of building systems and an analytical framework that connects energy and IEQ at the operational level, and not just the design phase.
Figure 3. Integrated data feedback loop for human-centred building operation. Sensor data feeds into integrated analysis, informing operational decisions, which produces measured outcomes that, after verification are fed back to refine the analysis.
One recurring insight from the interviews is that data alone does not improve buildings. What matters is interpretation. Many facility teams already have access to large volumes of operational data but lack the capacity or tools to translate it into decisions.
Dashboards often prioritise visualisation over understanding. They show trends but rarely explain causes or consequences. As a result, interventions remain reactive: a complaint leads to a setpoint change; a high energy bill triggers generic savings measures that may or may not address the underlying problem.
A decision-oriented approach uses operational data differently. It explores ‘what-if’ scenarios before changes are implemented. It evaluates trade-offs between comfort, energy and investment. Several transcripts describe the value of using operational data as input for simplified digital models that test alternative control strategies or system configurations without the cost and risk of live experimentation.
This does not require fully detailed simulations. What is needed is a credible representation of the building’s actual behaviour, grounded in measured data, that supports informed choices, and is accessible enough to be used by the people responsible for day-to-day operation.
Figure 4. From operational data to intervention scenarios using a digital twin. Real building data is fed into a digital twin to test intervention scenarios before implementation. The result is more precise decision-making.
Human centred buildings cannot be understood without considering occupancy and behaviour. Yet, these factors remain among the least accurately represented in design and operation.
Interviewees repeatedly point out that actual occupancy levels are often significantly lower or more variable than assumed, especially in offices and educational buildings. This has profound implications for both energy use and IEQ. Systems designed for peak occupancy may operate inefficiently under partial load, while comfort issues may arise in sparsely occupied zones due to inappropriate control strategies.
Operational data makes these dynamics visible. By correlating occupancy signals with indoor conditions and energy use, it becomes possible to identify mismatches between system operation and actual use. This opens the door to targeted adjustments, such as zoning strategies responsive to actual space use, demandcontrolled ventilation matching the real occupancy, or revised schedules that reflect how buildings are used rather than how they were intended to be used that improve both comfort and efficiency.
Another key theme emerging from the interviews is the limitation of traditional commissioning. Commissioning is often treated as a project phase rather than an ongoing process. Once formal handover is completed, systematic performance verification largely stops.
Several practitioners argue for a continuous commissioning mindset, in which operational data is used to verify and refine performance over time. This is particularly relevant in the context of human centred buildings, where comfort outcomes depend on evolving use patterns and expectations.
Continuous commissioning does not imply constant intervention. Rather, it involves monitoring key performance relationships and intervening when deviations indicate emerging risks to comfort or energy performance.
Figure 5. Data-driven services across the full building life cycle. Three key intervention points are where operational data analysis adds direct value: grid congestion and commissioning, post-occupancy optimisation, and portfolio and energy management.
While much discussion focuses on individual buildings, many owners, public authorities and real estate managers operate at a portfolio scale. At this level, the separation between energy and IEQ becomes even more problematic. Aggregated energy data may suggest acceptable performance, while local comfort issues remain hidden.
Figure 6. Energy labels do not guarantee operational performance. Buildings with better energy labels can consume more energy than they should. The gap between certification and actual performance is where the risk lies.
Interview material highlights the importance of consistent indicators that allow comparison across buildings while retaining sensitivity to user experience. Portfolio-level strategies benefit from identifying patterns: which building types or system concepts systematically underperform in terms of comfort per unit of energy and under which conditions?
A human centred perspective encourages portfolio decisions that prioritise robustness and adaptability rather than purely nominal efficiency.
Figure 7. Not all sustainability interventions are the same. Measures with high consumption and strong cost-effectiveness demand immediate action; those with low consumption and poor return are less urgent.
The EPBD recast sets ambitious goals, but its success depends on implementation. Operational data can play a critical role in bridging the gap between policy intent and real outcomes.
By demonstrating how energy and IEQ interact in practice, data‑driven approaches can inform realistic benchmarks, support compliance strategies and provide feedback for future regulation. Several interviewees stress that without such feedback loops, there is a risk of repeating the same performance gaps at scale.
Human centred buildings require evidence not only that energy targets are met, but that indoor environments genuinely support the health and wellbeing of the people who use them.
Human-centred buildings cannot be achieved through technology alone, nor through regulatory compliance alone. They require a shift in how performance is defined, monitored and managed across the building life cycle. Energy use and indoor environmental quality are not competing objectives, but interconnected outcomes of how buildings are designed, operated and used. Managing them in isolation produces buildings that optimise for neither.
Operational data provides the missing link. When energy and IEQ data are analysed together, in relation to real occupancy and behaviour, buildings can be managed proactively rather than reactively. This supports better comfort, more resilient energy performance and more informed decision-making over the building life cycle.
As the EPBD places greater emphasis on IEQ, the challenge is no longer whether data is available, but whether it is used to understand buildings as lived environments. Bridging energy and IEQ through data is therefore not a technical detail, but a prerequisite for genuinely human-centred buildings.
