Heating monitoring provides transparency regarding the actual operation of the system. Adaptive distribution systems, such as smart thermostats, help to translate this transparency into more stable and efficient heat distribution. Nevertheless, one key parameter of the heating system remains untouched if optimisation is not approached holistically: the heating curve. It plays a decisive role in determining the temperature at which a system operates – and thus influences both energy consumption and operational reliability.

The heating curve itself is a fixed control variable. It describes the relationship between the outside temperature and the required flow temperature, so it cannot learn and adapt. In practice, however, it is evident that many existing heating curves do not match the actual building. They are often set too steeply, remain at a consistently high temperature level, or have not been reviewed for years. The consequences are unnecessary energy consumption, unstable control conditions and reduced effectiveness of downstream optimisation measures.

This is precisely where data-driven decision support comes in. The aim is not to automate the heating curve itself or make it 'learning', but rather to establish robust settings for it. Monitoring data provides the necessary transparency for this. It shows how the flow and return temperatures actually behave, how stably the system reacts to changes in the outdoor temperature, and whether the operating temperature is consistently higher than necessary. When supplemented by information from the distribution level — for example, from smart thermostats — a consistent picture emerges of how well the interplay between generation, distribution and consumption is functioning.

Based on this information, it is possible to draw well-founded conclusions as to whether a heating curve should be adjusted and, if so, in which direction. Digital systems can identify typical patterns, such as excessively high flow temperatures during mild weather, sluggish system responses or unnecessary safety margins that have built up over time. While the decision on whether to adjust the curve remains with the technical operations team, it is now data-driven rather than based on gut feeling.

This approach is particularly relevant for the housing sector as it strikes a balance between operational reliability and efficiency. An overly aggressive reduction in the heating curve can cause comfort issues, while a setting that is too conservative leads to permanently increased energy consumption. Data-driven decision support can help resolve this conflict of objectives. It identifies areas where adjustments can be made and where systemic limits have been reached, for instance due to structural conditions or hydraulic restrictions.

The interaction of all digital levels is crucial here. Monitoring alone identifies deviations, but cannot contextualise them. Adaptive distribution systems stabilise operations, but cannot replace a systemic assessment. It is only by combining these two perspectives that it becomes possible to view the heating curve within the context of the entire system. This transforms an isolated control variable into a consciously managed operational element whose settings can be comprehensively justified and documented.

The added value lies in the quality of ongoing operation. Regularly reviewing and adjusting heating curves on the basis of real operating data helps to use energy more efficiently, reduce the load on systems, and increase the effectiveness of further measures. At the same time, robust documentation is created, which is also gaining importance in regulatory and ESG contexts.

This brings this series of articles full circle. Heating monitoring creates transparency. Adaptive distribution systems then translate this transparency into tangible results. Finally, data-driven decision support ensures that key control parameters, such as the heating curve, are set realistically and responsibly. Together, these building blocks do not represent a theoretical ideal state, but rather a pragmatic approach to achieving more efficient and stable heating operations.