Adaptive hydraulic balancing: Why learning systems are necessary in existing buildings

Heating monitoring creates transparency about actual system operation. It shows where systems are inefficient, where settings are incorrect and where energy is being lost. But transparency alone does not change anything. The crucial question is: How can these findings be effectively transferred into ongoing operations in a heterogeneous building stock? A key variable here is hydraulic balancing – a well-known tool that is often inadequately implemented in practice.

Classic hydraulic balancing is based on static assumptions. It requires that building geometry, pipe routing, radiator outputs and usage profiles are known and stable. In reality, this is rarely the case. Buildings have been modified over decades, flats have been used differently, and pipe networks have been expanded or simplified. The target state calculated once often does not adequately reflect actual operation. In addition, even a correctly performed balancing remains a snapshot. It does not respond to changed usage conditions, weather effects or seasonal shifts in load behaviour.

Against this backdrop, adaptive approaches are gaining in importance. Smart thermostats enable hydraulic balancing that is not based on theoretical models, but on actual measured operating conditions. They continuously record how rooms heat up, how long radiators continue to run and how stable target temperatures are achieved. On this basis, they dynamically regulate the flow and continuously adjust it to actual requirements. Balancing thus becomes an ongoing optimisation process rather than a one-off project.

The particular advantage of this approach lies in its proximity to the actual point of use. Thermostats are located where comfort and energy consumption directly coincide. They represent the final distribution level in the heating system and thus provide valuable feedback on the behaviour of the entire network. When combined across many residential units, this creates a decentralised sensor system that allows conclusions to be drawn about hydraulic imbalances, oversupply or structural weaknesses – even when central system data alone does not provide a clear picture.

This pragmatic approach proves particularly effective in existing buildings. Adaptive thermostats can be used without interfering with the central technology and can be rolled out step by step. They improve heat distribution, reduce extreme deviations between flats and stabilise operation under real everyday conditions. In doing so, they replace neither the central control system nor the heat generation. They have a complementary effect – where classic optimisation measures reach their practical limits.

The interaction of the systems is crucial. Monitoring shows where inefficient operating conditions arise and how measures are effective. Adaptive thermostats implement these findings in a decentralised manner and ensure that efficiency gains actually reach the building. The two levels are not alternatives, but are interdependent. Without monitoring, there is no classification; without adaptive distribution, optimisation often remains theoretical.

In terms of energy, the effect is evident in the sum of small corrections. Adaptive systems reduce typical losses caused by misallocation, oversized flow rates or sluggish responses. They improve thermal comfort and help to use energy where it is needed. For the housing industry, this provides a realistic way to leverage efficiency potential in existing buildings without extensive structural interventions or long project durations.

Adaptive hydraulic balancing thus marks an important stage in the development of data-based heating operation. It translates transparency into action and lays the foundation for the next step: the systemic optimisation of the system as a whole. When monitoring data and information from the distribution level are combined, the result is a digital image of the heating system that goes far beyond individual optimisations.

This is precisely where part 3 of this series comes in. In it, I address the question of how real operating data can be used to make informed decisions about setting and optimising the heating curve, what role algorithmic models play in this, and how individual digital building blocks can be combined to form a learning overall system.