Yes, one of the most rewarding—and unexpected—successes using Generative AI came during the integration of a lightweight AI algorithm into a SCADA environment for an energy monitoring project aligned with COP16 goals.
🌟 Success Highlight:
We used a simple Gen AI model hosted within a containerized Anaconda environment (orchestrated with Docker Compose) to analyze power consumption trends from InfluxDB time-series data. The AI-generated insights were then visualized through Node-RED dashboards. Unexpectedly, the model helped uncover hidden inefficiencies in the load distribution during peak hours, which led to a 12% reduction in energy waste over two months.
📊 Role of Data:
High-quality SCADA data—particularly voltage, current, and power factor—was essential. Preprocessing and normalization were critical to ensure the model could learn from operational behavior and not just noise.
🚧 Challenges:
Data readiness: Even though SCADA generates rich data, it wasn’t always clean or labeled well enough for AI use.
Integration limitations: Industrial protocols and real-time requirements made us rethink how fast and light the Gen AI model needed to be.
Trust: Convincing plant engineers to trust and act on AI-generated suggestions required explainable outputs and a lot of testing.
✅ Benefits Realized:
Faster diagnostics of anomalies in energy use
Empowered operators with predictive alerts via intuitive dashboards
Cost savings through actionable insights without heavy infrastructure investment
These outcomes are pushing us to explore even more embedded AI use cases within control systems and digital twins. I’d love to hear how others are balancing innovation and reliability in Gen AI projects!
PMI Team Member
