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Powering Digital Transformation: How Uniper Leveraged Advanced Analytics to Optimize Energy Operations

Executive Summary

Uniper, a leading European energy provider with 7,000 employees operating across more than 40 countries, has been at the forefront of the continent’s evolving energy landscape. Faced with mounting regulatory pressures, geopolitical factors, and the need to transition away from coal, Uniper sought to modernize its asset operations. 

With a complex portfolio that includes conventional power generation, gas trading, LNG, hydrogen, and renewables, the company turned to the Seeq Industrial Analytics & AI platform’s advanced analytics capabilities to tackle key operational challenges. By replacing outdated Excel-based workflows with Seeq’s integrated tools, Uniper realized measurable gains in asset performance, predictive maintenance, and workforce efficiency.

This paper highlights Uniper’s journey, focusing on two pivotal use cases—efficiency optimization in gas-fired power plants and predictive maintenance in coal-fired plants—while offering a roadmap for digital transformation in energy operations.

The Challenge: Ensuring Reliable, Efficient Operations in a Transitioning Energy Landscape

Europe’s energy sector is being reshaped by climate policy, coal phase-outs, and the ongoing energy crisis. For utilities like Uniper, these pressures elevate the role of conventional generation. Gas- and coal-fired plants remain essential for providing stability to the grid and securing revenue streams during volatile market conditions.

With this context, Uniper’s Energy Asset Management team operates under the vision of “Living Beyond Zero – assets, planet, and people.”  Their mandate is to safeguard operational performance, ensure compliance, and steer the cost competitiveness of assets while enabling the energy transition.   

At the operational level, this means tackling challenges such as: 

  • Maximizing the efficiency of gas turbines that must operate across varying load regimes to deliver both flexible and reliable power to the grid and Uniper’s customers. 
  • Enhancing the reliability of coal-fired plants, where failures such as boiler tube leaks can lead to costly downtime and undermine system stability. 

These pressures required Uniper to move beyond fragile Excel-based engineering methods and embrace advanced analytics to meet rising expectations for performance, reliability, and resilience.

Operational Challenges

Uniper manages a diverse 22.4 GW power generation portfolio that spans gas, coal, hydro nuclear, and renewable generation plants, alongside trading and LNG infrastructure. While the macro drivers of the energy transition set the strategic direction, day-to-day success depends on ensuring that conventional assets remain efficient, reliable, and cost-competitive. 

At the operational level, two pressing challenges stand out: 

Challenge 1: Degrading Performance in Gas-Fired Power Plants 

  • Operation across varying loads and ambient conditions makes performance analytics complex (“comparing apples to apples”). 
  • Excel-based methods struggled to handle high-frequency datasets and accurately benchmark actual vs. modeled output. 
  • Engineers needed faster root-cause identification to validate post-outage improvements and sustain reliable generation. 

Challenge 2: Boiler Tube Failures in Coal-Fired Power Plants 

  • Monitoring hundreds of boiler wall sensors (450+ per unit) generates massive amounts of data that traditional tools could not efficiently process. 
  • As a result, temperature excursions driving tube failures were difficult to detect, measure, and anticipate. 
  • Failures forced shutdowns of large 1,100+ MW units—causing days of downtime and lost revenue during periods when reliability of conventional assets is most critical. 

The Solution: Strategic Implementation of Advanced Analytics

To address these operational challenges, Uniper adopted Seeq as its advanced analytics platform for time-series data. Implementation was driven by the centralized Energy Asset Management team, which worked closely with site engineers to contextualize operating data. Training (in both English and German), analytics office hours, and direct support from Seeq Analytics Engineers ensured adoption across teams.  

Beyond the platform rollout, the value came from applying analytics directly to two critical use cases: 

Gas-Fired Power Plants – Performance Optimization

  • Engineers used Seeq to build multivariate models and performance curve fitting that automatically updates as new data streams in. 
  • Enabled exception-based monitoring, making deviations in performance easy to detect and analyze at a glance. 
  • Provided easy review of load-dependent efficiency and the impact of modifications or outage. 
  • Designed reusable analyses that could be scaled across sites, ensuring consistency and reducing duplication of effort. 

Coal-Fired Power Plants – Predictive Maintenance

  • Leveraged Seeq Workbench and Seeq Data Lab to perform calculations across 450+ boiler wall sensors, eliminating the limits of manual site-level analysis. 
  • Set up exception-based monitoring to detect deviations from boiler design parameters in real time. 
  • Applied capsule- and value-search logic to categorize excursions and provide early warning of tube leak risk. 
  • Built monitoring workflows that improved engineers’ ability to track stress on boiler membrane walls and assess how temperature excursions accelerate creep damage. 
  • Provided a quick, repeatable framework for analysis that democratized visibility across the fleet rather than leaving insights siloed at individual sites. 

The Results: Measurable Efficiency, Reliability, and Organizational Impact

Gas-Fired Power Plants – Performance Optimization 

  • Achieved clear, load-dependent efficiency benchmarks that were previously impossible with Excel. 
  • Validated performance improvements after outages and upgrades, giving confidence in maintenance investments. 
  • Strengthened compliance reporting and improved coordination with grid operators. 
  • Scaled analyses across multiple plants, establishing a consistent approach to performance monitoring. 

Coal-Fired Power Plants – Predictive Maintenance 

  • Gained a fleet-wide view of boiler wall stress, improving the ability to manage the risk of tube failures. 
  • Advanced predictive insights — able to determine where leaks are most likely to occur. 
  • Enabled condition-based maintenance, minimizing unplanned unavailability and aligning maintenance schedules with actual equipment condition. 
  • Shifted from reactive, site-level monitoring to proactive, centralized risk management. 

Organizational Impact and Roadmap

  • Reported significant time savings across central and site teams by replacing manual analyses with reusable templates. 
  • Democratized plant visibility, making advanced analytics accessible beyond a handful of site engineers. 
  • Informed asset and maintenance planning strategies, embedding predictive analytics into fleet operations. 
  • Future development focuses on: 
  • Root cause analysis for boiler excursions. 
  • Machine learning to forecast failure timing. 
  • Energy management improvements during standby periods. 
  • Systematic bad actor analysis to address recurring equipment issues.

Conclusion: A Blueprint for Energy Sector Digital Transformation

Uniper’s structured use of Seeq illustrates how centralized governance, robust training, and local collaboration can successfully drive digital transformation. The result is enhanced performance, reduced risk, and a scalable model that other utilities can follow in their own energy transition journeys.