Adaptation of the heating curve for heating systems

An efficient energy supply with low consumption is becoming increasingly relevant to reduce operating costs and minimize environmental impact. There is considerable potential for savings, especially in the German building sector, which has numerous existing buildings with high energy consumption due to outdated insulation and heating systems. Therefore, effective and cost-effective measures are necessary, especially in this area, to reduce energy demand and associated emissions. However, renovations and new technologies face low acceptance due to high investment costs.

Therefore, an innovative control method has been developed that autonomously recognizes the current thermal demand of a building and adjusts the performance for its water-based heating automatically, without the need for component replacement or sensor installation. Rather, efficiency gains are achieved through optimized control strategies.

To determine the current thermal energy demand of a building, a weather-compensated control system is typically used. Each outside temperature is assigned to a corresponding set inlet temperature (so called heating curve). Changes to the building envelope, system technology, or usage require manual adjustment of the heating curve settings, which often go unaddressed. An analysis of existing buildings has also shown that many heating curves are set too high, resulting in reduced system flow rates. In the end, this leads to an on-off operation of the control components, especially the boiler. Due to its modulation limit the boiler cannot provide too low reduced heating capacity demands, leading to frequent intermittent mode, increased emissions, and inefficient operation.

A self-learning control algorithm is intended to counteract the behavior described by continuously adapting the set inlet temperature to the actual requirement. The method utilizes central parameters such as flow rate, forward and return temperatures, and burner output, which are already available at the heat generator. Through continuous analysis of behavior patterns and sequential consideration, the control algorithm automatically adjusts the heat curve, ensuring continuous optimization of heating performance, even with subsequent changes to the building or technical system. When development the adaptive controller, the focus is not only on stable control and increased efficiency, but also on user comfort. Therefore, sufficient heat must be available at all times to maintain the desired room temperature.

Results show a reduction in overall energy demand of up to 10% and a decrease of cyclic operation up to 50%, while maintaining user comfort with an average reduction of the heat curve of 8 K without any complaints made by users.