Efficient Integration of Machine Learning into District Heating Predictive Models

Energies

Jiří Pospíšil¹  Libor Kudela¹  Radomír Chýlek¹ 

[1] Energy Institute, Faculty of Mechanical Engineering, Brno University of Technology—VUT Brno, Technická 2896/2, 61669 Brno, Czech Republic;

关键词: district heating;    machine learning;    optimization;    modelling;    dynamics;    pipes;   

DOI  :  10.3390/en13236381

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown