Watts Up? Leveraging AI & Data for Energy Management
summary
Pilot at Bosch Rexroth’s Ulm model factory builds a live link between an asset orchestration/Factory Orchestration Platform and Splunk/Cisco analytics, ingesting device-level telemetry plus Electricity Maps forecasts to run ML models for pricing (peak management), availability (standby) and timing (scheduling). Team targets the single most energy‑intensive machine, uses Splunk sustainability toolkit and Machine Learning Toolkit, and reports validated results: energy cost down 20–30%, consumption down 10–15%, CO2e down 25–30%.
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chapters (9)
- 0:00Energy management with AI
- 1:06Industry 4.0 challenges
- 4:31Orchestration platforms
- 6:44Energy management in manufacturing
- 8:09Key metrics for energy systems
- 10:12Visibility and timing in optimization
- 12:09Energy monitoring insights
- 13:16Peak management and scheduling
- 14:48Energy efficiency results
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