Heating monitoring creates transparency about actual plant operation. Adaptive distribution systems such as smart thermostats help to translate this transparency into more stable and efficient heat distribution. Nevertheless, one key variable in the heating system remains untouched if optimisation is not considered holistically: the heating curve. It determines the temperature level at which a system operates – and thus both energy consumption and operational reliability.

The heating characteristic curve itself is a fixed control variable. It describes the relationship between the outside temperature and the required flow temperature and is therefore not a learning element. In practice, however, it has been shown that many heating characteristic curves in existing buildings do not match the actual building. They are set too steeply, are permanently at too high a temperature level or have not been checked for years. The consequences are unnecessary energy consumption, unstable control conditions and limited effectiveness of downstream optimisation measures.

This is exactly where data-based decision support comes in. The aim is not to automate the heating characteristic curve itself or make it ‘learning’, but to put its setting on a reliable footing. Monitoring data provides the necessary transparency for this: it shows how the flow and return temperatures actually behave, how stable the system is in response to changes in outside temperature, and whether the temperature level during operation is permanently higher than necessary. Supplemented by information from the distribution level – for example, from smart thermostats – this provides a consistent picture of how well the interaction between generation, distribution and use is functioning.

On this basis, informed statements can be made about whether a heating characteristic curve should be adjusted and in which direction. Digital systems can help to identify typical patterns: for example, oversized flow temperatures at mild outside temperatures, sluggish system responses or unnecessary safety margins that have developed over time. The decision on whether to make an adjustment remains deliberately with the technical operations department – but it is no longer made on gut instinct, but rather based on data.

This approach is particularly relevant for the housing industry because it addresses the balancing act between operational reliability and efficiency. An overly aggressive reduction in the heating curve can cause comfort problems, while an overly defensive setting leads to permanently increased energy consumption. Data-based decision support helps to resolve this conflict of objectives objectively. It reveals where there is room for manoeuvre and where systemic limits have been reached – for example, due to structural conditions or hydraulic restrictions.

The interaction of all digital levels is important here. Monitoring alone shows deviations, but cannot classify them. Adaptive distribution systems stabilise operation, but do not replace systemic evaluation. Only the combination of both perspectives makes it possible to view the heating curve in the context of the entire system. This transforms an isolated control variable into a consciously managed operating element whose settings are comprehensibly justified and documented.

The added value lies in the quality of ongoing operation. Heating curves that are regularly checked and adjusted on the basis of real operating data help to use energy more efficiently, reduce the load on systems and increase the effectiveness of further measures. At the same time, reliable documentation is created, which is also becoming increasingly important in a regulatory and ESG context.

This brings this series of articles full circle. Heating monitoring creates transparency. Adaptive distribution systems translate this transparency into action. Finally, data-based decision support ensures that key control variables such as the heating curve are set realistically and responsibly. Together, these building blocks do not represent a theoretical ideal, but a pragmatic, implementable path to more efficient and stable heating operation.