Buildings are generating more and more data. Energy flows, climate data and installation statuses are often already available. Yet in practice we see that this data does not always lead to more control or better performance. That’s where GACS comes in.
GACS (Building Automation and Control Systems) is a European requirement for large buildings, utility buildings. The goal: better monitoring of energy use, smarter control of installations and faster detection of energy loss. But GACS is not just about measuring. It is primarily about demonstrable insight into how a building functions. And that is precisely where the challenge lies for many organizations. A lot of data is available, but without analysis that information remains unused.
GACS is therefore shifting the focus: from collecting data to understanding and using data. Not as an extra administrative burden, but as an opportunity to get a structural grip on energy use and installations.
When data provides no direction
Data has been collected in many buildings for years. Yet important questions often remain unanswered.
Why do spikes in energy consumption occur?
Which plants cause these spikes?
And when does behavior deviate from what is normal for this particular building?
Without context, data is difficult to interpret. Values seem to fluctuate, but the frame of reference is missing.
What is normal use?
What belongs to the building, plant or use profile?
Analysis therefore begins not with optimizing, but with understanding what you see.
Analysis as a foundation for grip and compliance
Within GACS, Data Science Lab focuses on analyzing and interpreting building data. By structuring data, making patterns visible and placing deviations in context, insight is created that goes beyond individual measurements or dashboards. This forms the basis for demonstrability and control.
That analysis does not stand alone.
Together with Montreal Solutions, we ensure that the technical reality of the building is accurately captured and monitored.
Montreal Solutions reliably maps energy systems and installations in detail – by system and by component. Based on this, Data Science Lab can make connections, compare behavior and identify performance degradation.
An example is recognizing peak loads. Not only making it visible that a peak is occurring, but also providing insight into which installations are contributing to it and under what conditions.
Another example is the identification of installations that structurally perform differently than expected, without being directly visible in total energy consumption.
Insight without assumptions
GACS requires demonstrability. This means that insights must be traceable and explainable.
Not based on assumptions, but on consistent analysis over time. That analysis creates a frame of reference: what is normal behavior, what deviates and where is adjustment needed?
This makes it possible to substantiate choices and take targeted action, rather than reactive management of incidents.
It provides peace of mind and overview – for managers, owners and supervisors.
GACS as an opportunity for structurally better management
GACS is thus not just a technical or legal obligation. It is also a practical framework for getting a handle on building performance. By putting analysis at the center, insight becomes the starting point for better decisions, structural monitoring and continuous improvement. Thus, GACS will not be a checklist, but a tool to demonstrably and manageably control energy use and installations.
Get in touch
Do you have building data, but not yet a clear understanding of energy use and installations?
Then analysis is often the missing step. Call Sebastiaan, +31642987624. We’ll show you how to get a handle on building performance with the right analysis while meeting GACS obligations.
