Overview
Used ML models grounded in thermodynamics to forecast power consumption, enabling real-time, proactive efficiency optimization.
Challenge
Organizations in power generation often face hurdles when applying machine learning to optimize operational efficiency:
- Data Fragmentation: Sensor data across units such as CO₂ compressors and turbines is scattered and inconsistently formatted, hindering analysis.
- Limited Domain Alignment: Off-the-shelf ML models fall short in capturing thermodynamic behaviors unique to power generation cycles.
- Delayed Decision-Making: Operators often lack real-time insights into power consumption trends, leading to reactive, rather than proactive, control.
Solution
- 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.
Key Benefits
- Proactive Operations: Accurate power consumption forecasts enable early adjustments to maintain optimal system performance.
- Custom Model Design: Each unit model reflects its real-world operating behavior, improving prediction reliability.
- Real-Time Results: Operators gain immediate visibility into consumption patterns and efficiency KPIs, supporting data-driven decisions.
