WHEN A DIGITAL TWIN BECOMES INTELLIGENT AI and District Heating are the future.
By Bo Stig, CCO and Engineer, Neurospace
Over recent years, Berliner Energie und Wärme (BEW) has undergone a significant digital transformation in its digital operations, resulting in the development of a digital twin. This twin calculates and simulates the district heating (DH) grid’s hydraulic capabilities, thereby enhancing overall performance and efficiency. However, based on hydraulic physics, it requires substantial resources to expand as the city’s grid grows. BEW and Neurospace identified an opportunity to utilize Artificial Intelligence (AI) to further enhance the digital twin by leveraging historical data to forecast power consumption and hydraulic limitations.
In collaboration with Neurospace, BEW’s progressive System Digitalisation Team identified an opportunity to expand the digital twin’s capabilities using a data-centric approach. This led to the development of two distinct use cases, enabling the digital twin to identify power consumption and hydraulic capabilities within the grid through the use of AI. These cases were viewed as a unique way to enrich and expand the digital twin by utilizing the patterns generated from the data. Quality over Quantity, forget Big Data. For this 2-month Proof of Concept (PoC), a clear scope, data quality, and availability were paramount. It was also crucial
to test the PoC in a smaller environment; thus, BEW and Neurospace agreed on the North-Eastern part of the grid for the two cases. The team identified available data and common data points across substations, noting that not all substations had the same data sets available. With more relevant data, the AI model was not only improved but also trained more quickly on-site, even on a laptop, eliminating the need for large data centers 1 . This approach acknowledges the evolving nature of the future, necessitating AI model retraining over time, a method significantly different from those used by OpenAI with ChatGPT and other large language models.
1 Large data centers rely on multiple GPUs, which are expensive to operate and generate significant CO2 emissions.
34 HOTCOOL SPECIAL COLLECTION edition 2, 2025
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