Design and Implementation of a Decision Integration System for Monitoring and Optimizing Heating Systems: Results and Lessons Learned

Kirill Djebko (), Daniel Weidner (), Marcel Waleska, Timo Krey (), Bhaskar Kamble, Sven Rausch, Dietmar Seipel and Frank Puppe
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Kirill Djebko: Department of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany
Daniel Weidner: Department of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany
Marcel Waleska: Department of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany
Timo Krey: RAUSCH Technology GmbH, Leightonstraße 3, 97074 Würzburg, Germany
Bhaskar Kamble: Senercon GmbH, Hochkirchstraße 11, 10829 Berlin, Germany
Sven Rausch: RAUSCH Technology GmbH, Leightonstraße 3, 97074 Würzburg, Germany
Dietmar Seipel: Department of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany
Frank Puppe: Department of Computer Science VI: Artificial Intelligence and Knowledge Systems, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany

Energies, 2024, vol. 17, issue 24, 1-28

Abstract: With the increasing need to tackle climate change, energy efficiency and reduced CO 2 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 CO 2 emissions.

Keywords: heating system optimization; artificial intelligence; decision integration system; decision support system; simulation; energy efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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