Success Stories

Optimizing Power Generation Efficiency with ML-Driven Consumption Forecasting

Power Generation

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Optimizing Power Generation Efficiency with ML-Driven Consumption Forecasting

Used ML models grounded in thermodynamics to forecast power consumption, enabling real-time, proactive efficiency optimization.

Goals

Forecast power consumption using thermodynamic ML models.
Enable real-time, proactive efficiency optimization across units.
Industrial power generation facilities operate highly complex thermodynamic systems where
Plant operators frequently struggle with fragmented data scattered across CO2

Challenge

Sensor data across compressors and turbines is fragmented.
Off-the-shelf ML models fail to capture thermodynamics.
Traditional data infrastructures store this information in inconsistent formats
Furthermore, standard off-the-shelf machine learning algorithms fall short because they

Solutions

Build simulation-driven ML models grounded in thermodynamics.
Embed predictive models into Seeq and AVEVA PI.
To bridge the gap between pure data science and physical plant operations
By grounding advanced machine learning models in both thermodynamic physics and

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Results

Enabled accurate power forecasts for proactive system adjustments.
Designed custom unit models reflecting real-world operating behavior.
Provided immediate visibility into efficiency metrics and KPIs.
These custom predictive models are then seamlessly integrated into established industrial

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