We collect data from IoT sensors installed in each distribution transformer and enrich it with past maintenance history to accurately predict high risk transformers that are likely to fail. Powered with this information, proactive maintenance of transformers can be done to reduce down-time by at least 10%.

With use of machine learning algorithm, we have improved forecast accuracy over traditional methods for Distribution companies. This has helped improve grid stability and reduce penalty by at least 15% as per prevailing ABT/DSM regulations.

We help Discoms prepare ARR using historical data to estimate future expenses for each category (Repair & Maintenace, Operations, Employees cost, Fixed cost etc).