Performance Increase on a Production Line
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Project - Data science
For the water distribution network operator, it is difficult to accurately estimate the water consumption for the days or even the hours to come. This can sometimes cause problems with supply limitations if the required volume of water is not available at a given time. In addition, leaks are not easily detectable on large-scale networks with permanent consumption.
Using historical water usage data, we were able to apply machine learning to model the water usage for each subnet of the overall distribution system. The resulting model made it possible to predict water consumption for the following 24 hours, with more than 85% of errors below the threshold of 2.5 m³/h (on networks with an average consumption of around 50 m³/h).
The use of such a prediction tool gives rise to multiple applications :
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