System for Prediction of Thermal Power

Thermal power company

Project overview

The client operates two power plants, generating electricity from thermal energy produced by burning fossil fuels. They sought a solution to classify equipment failures, predict the time to breakdown, and optimize water supply systems in their thermal power plants.

Project process

The challenge was to reduce the probability of equipment breakdowns and failures in their thermal power plants by predicting and optimizing their operation.


Our team developed an advanced analytics system using machine learning algorithms to optimize the operation of the power plants, predict and prevent equipment failures and breakdowns, and utilize real-time data from over 2,000 sensors per block.

Amazon Web Services API Gateway, Lambda, Kinesis, ECS, EC2, DocumentDB, Redis, InfluxDB, S3, EMR
5 people


Since the system was implemented in 2020, the client has been able to avoid fines in the energy market and minimize damage from emergency equipment shutdowns. The predictive maintenance system reduced unplanned downtime, lowered maintenance costs, and improved overall operational efficiency.

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