Optimize Hydroelectric Plant Performance with H-EM:
the Advanced SaaS for Accurate Signal Analysis, Predictive Maintenance, and Custom Machine Learning Models

H-EM is a SaaS dedicated to the management of hydroelectric plants.

The main objective of this solution is to provide a detailed and accurate analysis of signals coming from the plants, while calculating the quality of received data and conducting performance analyses of the plant, including predictive maintenance. By utilizing machine learning models, it is possible to predict and prevent abnormal plant conditions, providing operators with the necessary tools to optimize performance and maintenance activities. Models can be tailor-made or customized and viewed through an intuitive dashboard.



Models can be tailor-made or customized and viewed through an intuitive dashboard.

  • Analysis of signals on interactive chart

  • Classification of signal quality

  • Signals comparative analysis

  • Group efficiency chart

  • Custom signal selection for predictive models

  • Custom model training duration

  • Real-time KPIs and alarm notifications

  • Identification of critical signals and comparison with simulated values

  1. Identification of defective sensors.
  2. Rapid detection of anomalies
  3. Reduction of management and maintenance costs (O&M).
  4. Plant efficiency monitoring.


Enhance your system with additional functionalities and maximize versatility.

You can choose one of our packages or integrate these plugs in into your existing system, discovering all the benefits of customization and plant optimization.



A very useful tool for remote supervision, avoiding additional costs for operators in overseeing the construction process of the hydroelectric plant. Thanks to the use of satellite images, periodic information on project milestone statistics is obtained. Remote supervision of the construction process of the hydroelectric plant

  1. Reduction of costs in documentation and operational management
  2. Improvement of information sharing among operators