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500% Boost in Predictive Model Performance

PS AI Labs and Devron created a predictive model that trained on the SCADA sensor data and achieved a 500% boost in predictive performance above the baseline. Thus enabling the client to identify issues more accurately and faster.

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Sira-Kvina Kraftselskap is a renewable energy production company. The company operates clean and renewable hydropower plants, serving customers in Norway.

Case Study at a Glance

Data Landscape

  • Disparate
  • Heterogeneous
  • Terabytes

Use Case

Predictive Maintenance




  • Unpredictable hydro generator downtimes
  • $1 million per day loss per down hydro generator
  • Negative customer sentiment & trust


  • 500% boost in predictive performance about the baseline
  • More accurate and earlier detection of component failure

Data Challenges

Pain Point Summary

Data Description


The Sira-Kvina power company is one of Norway’s primary renewable energy power providers, producing approximately 5% of the country’s power from seven hydropower plants. Sira-Kvina struggled to tie their data together from disparate and heterogeneous operational systems and thus could not make informed decisions. On average, one of the hydro generators going down creates a loss of approximately $1 million per day, as well as negative customer sentiment and trust. Sira-Kvina needed a trusted data and analytics partner and engaged PS AI Labs & Devron to design and deploy predictive models to determine hydro generator component failure risk to enable preemptive maintenance operations and system monitoring.


Data Challenges

Data Challenges

Disparate Data Sources
Heterogeneous Schema
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Data Description

Data Description

Terabytes of Data
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PS AI Labs' dedicated data science and engineering team worked hand-in-hand with Sira-Kvina leaders, operators, and stakeholders to design and build machine learning models using various operational and maintenance data sources. Using the Devron federated machine learning platform our team developed an abstract model of the generator systems that could be linked to key operational and sensor data systems. Our focused data discovery and exploration identified that the generator had moved into a damaged state eight power cycles earlier than had previously been observed by the Sira-Kvina maintenance team.


PS AI Labs & Devron created a predictive model that was trained on the SCADA sensor data and achieved a 500% boost in predictive performance above the baseline. Thus, our models helped Sira-Kvina operators identify issues more accurately (fewer false positives and false negatives) and much earlier, allowing operators to strategically approach each maintenance issue in a cost-optimal fashion. Sensor metrics were reduced from hundreds to ten, which provided the best predictive performance, making the results easier to interpret and more actionable for the business.

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Data Challenges

Data Description


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