Optimizing Power Generation Efficiency with ML-Driven Generation Output Forecasts
BKOAI developed thermodynamics-informed machine-learning models to forecast power-generation output and support proactive dispatch and efficiency decisions
Goals
Forecast generation output using thermodynamics-informed machine learning models.
Enable proactive efficiency optimization across generating units.
Consolidate fragmented operating data from compressors, turbines, and related equipment.
Give operators timely visibility into generation output forecast and plant-performance indicators.
Challenges
Data Fragmentation: Operating data across generating units is scattered and inconsistently formatted, hindering analysis.
Limited Domain Alignment: Off-the-shelf machine-learning models may not capture thermodynamic behavior unique to power-generation cycles.
Delayed Decision-Making: Operators often lack timely insight into generation-output forecasts, leading to reactive rather than proactive decisions.
Solutions
Template-Driven Contextualization: We built simulation-driven ML models grounded in thermodynamics and historical time series data to capture the nuanced operation of each unit.
Seamless Integration: These models were embedded into industrial platforms like Seeq and AVEVA PI, allowing for continuous prediction and efficiency monitoring.
Results
Generation Output Forecasts: Produced forecasts that supported proactive operating adjustments.
Custom Model Design: Each unit model reflects real-world operating behavior and plant-specific thermodynamics.
Real-Time Visibility: Operators gained timely visibility into generation output forecasts and plant-performance indicators.
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