Analysing district heating data with artificial intelligence saves time and money
District heating systems may be complex systems but, in many cases, they can be optimised by correctly adjusting the building's heating installations. AI can help building owners identify potential improvements based on heat meter data alone.
Analysing district heating data with artificial intelligence saves time and money.
District heating systems may be complex systems but, in many cases, they can be optimised by correctly adjusting the building's heating installations. However, this requires regular analyses of the systems – which can be difficult and time-consuming. Artificial intelligence can help building owners identify potential improvements based on heat meter data alone. By that, the most effective measures can be prioritised and implemented.
Optimising the return temperature to avoid penalties.
An important topic within the optimisation of district heating installations in building stock is the optimisation of the return temperature. Most district heating companies calculate an "incentive fee" – commonly referred to as a penalty fee – if the return temperature is too high.
Below is data on a building with poor cooling during the summer shutdown. This building pays more than 50,000 DKK annually in incentive fees. Which is automatically detected and reported by our virtual district heating advisor.
Setting operating times to improve consumption.
Just as the penalty fee is calculated automatically, it is also possible to optimise the operation of the district heating systems by changing its operating times. For example, based on analyses of similar buildings that are registered on the Ento system, it is possible to suggest to building owners to turn down the heating on weekends for some of the buildings. Here, the annual savings can also be automatically calculated:
Further measures to decrease heat consumption in buildings.
In addition, heat consumption can of course be reduced by increasing the efficiency of the heating system itself, or the building envelope. Low efficiency systems can be found by analysing the heating efficiency based on available building information and comparing with other similar buildings.
Further optimisations can be found in incorrect set points and skewed or missing summer shut-offs.
Read more about Ento’s district heating advisor and find out how it can quickly provide an overview of possible savings across all your buildings.
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