H2O Agents Optimize Power Plant Output with DAI

This lesson walks through a hands on demo where an h2oGPTe agent takes on the role of a power plant performance analyst, working with sensor readings such as temperature, ambient pressure, humidity, and exhaust vacuum to predict net hourly electrical output. The agent validates data quality, analyzes seasonal and daily patterns, calls H2O Driverless AI to forecast output, and returns recommendations for optimal operating parameters and alert thresholds. Running the agent at its maximum accuracy setting, the lesson reviews the resulting charts and model score, and notes the importance of checking agent outputs before trusting them.