About Neurospace
About Berliner Energie und Wärme (BEW)
Neurospace was founded on the principle that existing data in DH, previously underutilized, could be leveraged. Therefore, Neurospace specializes in using AI on this data to enhance performance and reduce emissions by minimizing overproduction, all while maintaining the paramount safety of supply.
Berliner Energie und Wärme (BEW) operates one of Europe’s largest and most complex DH systems. It is not only separated into several grids but also varies significantly in its technical construction. It comprises over 25,000 substations across four different grids, supplying DH to approximately 700,000 households.
This model would identify situations where known weak points could lead to hydraulic limitations, resulting in households losing heat during peak times. Addressing these weak points is crucial for lowering the temperature of the supply heat and, consequently, reducing Berlin’s district heating production. Two models were developed using operational data, smart meter readings, and weather forecasts. The first model, designed to predict pressure differences at key network points, achieved an accuracy of ±0.1 bar. The second model, which forecasted the supply water temperature, was accurate to within ±3.53ºC. Both models performed effectively even with novel data. While adept at predicting system behavior within normal operating ranges, the interconnectedness of the heating system, where changes in one part affect others, poses a challenge for the current models to fully comprehend causal relationships. Although deemed successful, further study is recommended to understand the individual effects of various factors better.
then use this data as metadata to provide an indication of the hydraulic limitations in that area to the operators.
The future of AI and DH is poised for significant growth, with immense potential for both BEW and the broader sector. AI should be considered the same as insulating houses: the more you invest, the less energy you need to match demands. While everyone is fascinated by chatbots, the DH sector could potentially be one of the sectors to show the true potential of this technology in the green transition. How to make a successful Proof of Concept using AI Developing a successful AI PoC requires careful planning and execution. Based on our experience, here are the key considerations: Be specific: Clearly define the direction of what you aim to solve, what constitutes success, and how success will be measured. Ensure data availability: It is paramount that data are accessible before starting, as many systems may not be designed for the current AI era and can bottleneck projects. Keep it simple: Prioritize simplicity and make necessary compromises instead of expending resources on a ”perfect” solution that is likely to change significantly before implementation. Embrace failure: Innovation inherently involves trial and error. Not all AI projects will succeed, but the successful ones will justify the effort. Innovation: AI is about working with people; having the right domain experts is crucial for success and will lower the amount of data that is needed.
Smaller implementations With two successful cases proving that it is possible to make predictions on heat load and weak points. At this stage, the foundations of the AI models are established, but they still need to be matured for production. This formed the basis for implementing a solution in a selected area where there was a hydraulic weak point and a substantial number of substations, making it suitable for a real-world application. This will enable the digital twin to make predictions on the heat load and
For further information please contact: bo.stig@neurospace.io
36 HOTCOOL SPECIAL COLLECTION edition 2, 2025
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