In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess “better or worse” system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption.

With the increasing need to tackle climate change, energy efficiency and reduced CO₂ emissions are proving to be one of society’s greatest challenges. Special consideration should be given to heating systems as they are prone to inefficiency due to non-optimal controller configurations and the shortage of experts or qualified technicians to optimize the operating behavior. Especially for residential heating systems, more often than not, the target metric is the achievement of specific heating and hot water temperatures by manual adjustments with limited sensor information and with little regard to efficiency. This presents potential for computer-aided optimization based on artificial intelligence techniques. In this paper, we presented a Decision Integration System that is interfaced with a data acquisition infrastructure and allows for the analysis of measured heating system data, the generation of recommended measures for efficiency improvement, and the simulative validation of recommended controller parameter changes. We presented different parts of the Decision Integration System, the interfaced data acquisition infrastructure, as well as the non-invasive sensor appliances used. We analyzed the measured data of real heating systems and evaluated our approach by generating the recommended measures based on rules created by heating system experts, which were then partially applied to the physical heating systems and partially evaluated in simulation. Finally, we compared long-term energy consumption data against the latest monitoring period after implementing the measures. Our results showed an average reduction in energy consumption of 24.52% across all considered buildings, corresponding to an approximate reduction of 8.12 tons of CO₂ emissions.

The concept of association rules is well--known in data mining. But often redundancy and subsumption are not considered, and standard approaches produce thousands or even millions of resulting association rules. Without further information or post--mining approaches, this huge number of rules is typically useless for the domain specialist -- which is an instance of the infamous pattern explosion problem. In this work, we present a new definition of redundancy and subsumption based on the confidence and the support of the rules and propose post-- mining to prune a set of association rules. In a case study, we apply our method to association rules mined from spatio--temporal data. The data represent the trajectories of the ball in tennis matches -- more precisely, the points/times the tennis ball hits the ground. The goal is to analyze the strategies of the players and to try to improve their performance by looking at the resulting association rules. The proposed approach is general, and can also be applied to other spatio--temporal data with a similar structure.