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Predictive maintenance for public lighting

A major international player in the energy world needed to understand how to optimize streetlight maintenance.

The probability of malfunctioning assets depends on environmental factors (wind, temperature range, distance from the sea) and characteristics of their materials.

We developed a decision support system (DSS) based on an AI algorithm in the form of an interactive interface. By entering geographic areas and lighting-related parameters, our customer can identify those light points that are most worth to be replaced.


Timely and detailed risk analysis for each light point
Savings on unnecessary repairs and replacements
Cost prediction and competitiveness in tenders