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Article

Efficient Integration of Machine Learning into District Heating Predictive Models

Energy Institute, Faculty of Mechanical Engineering, Brno University of Technology—VUT Brno, Technická 2896/2, 61669 Brno, Czech Republic
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Energies 2020, 13(23), 6381; https://doi.org/10.3390/en13236381
Received: 13 November 2020 / Revised: 26 November 2020 / Accepted: 28 November 2020 / Published: 2 December 2020
Modern control strategies for district-level heating and cooling supply systems pose a difficult challenge. In order to integrate a wide range of hot and cold sources, these new systems will rely heavily on accumulation and much lower operating temperatures. This means that predictive models advising the control strategy must take into account long-lasting thermal effects but must not be computationally too expensive, because the control would not be possible in practice. This paper presents a simple but powerful systematic approach to reducing the complexity of individual components of such models. It makes it possible to combine human engineering intuition with machine learning and arrive at comprehensive and accurate models. As an example, a simple steady-state heat loss of buried pipes is extended with dynamics observed in a much more complex model. The results show that the process converges quickly toward reasonable solutions. The new auto-generated model performs 5 × 104 times faster than its complex equivalent while preserving essentially the same accuracy. This approach has great potential to enhance the development of fast predictive models not just for district heating. Only open-source software was used, while OpenModelica, Python, and FEniCS were predominantly used. View Full-Text
Keywords: district heating; machine learning; optimization; modelling; dynamics; pipes; smart systems district heating; machine learning; optimization; modelling; dynamics; pipes; smart systems
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MDPI and ACS Style

Kudela, L.; Chýlek, R.; Pospíšil, J. Efficient Integration of Machine Learning into District Heating Predictive Models. Energies 2020, 13, 6381. https://doi.org/10.3390/en13236381

AMA Style

Kudela L, Chýlek R, Pospíšil J. Efficient Integration of Machine Learning into District Heating Predictive Models. Energies. 2020; 13(23):6381. https://doi.org/10.3390/en13236381

Chicago/Turabian Style

Kudela, Libor, Radomír Chýlek, and Jiří Pospíšil. 2020. "Efficient Integration of Machine Learning into District Heating Predictive Models" Energies 13, no. 23: 6381. https://doi.org/10.3390/en13236381

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