1. How Machine Learning & Analytics Saved 1 Billion kWh
Cliff Federspiel, PhD
President and CTO, Vigilent

2. Surging Data & Energy Demand
11/5/2017
Surging Data & Energy Demand
40 ZB
Data volumes will double every 2 years, reaching 40 ZettaBytes by 2020 2%
Data centers consume more than 2% of the total US energy (about 70 billion kilowatt hours)
40%
Cooling accounts for 40% of all data center energy consumed
Confidential

3. Solve Difficult Cooling Challenge with Data
Control system activated
Design standards provide more cooling than needed Airflow complexity and IT variability make it impossible to manually optimize The result is wasted energy and lost capacity
Dynamically match cooling to IT load

4. Dynamic Control with Adaptive AI Optimizes Cooling
Measure heat load and cooling equipment efficiency
Learn effects of control actions
Model cooling airflow influence
Control cooling equipment
Predict how to optimize cooling

5. IoT Architecture with Machine Learning
Control
Prescriptive Analytics
Rack Sensors
Collect temperature
Gateways
Manage wireless communication
AI Engine*
Aggregates data. Learns. Issues control commands.
Data Engine
Analyzes cooling with predictive models
“Insights to Action”
Analytics UI
Inform decision making
Optimize Cooling Capacity
Reduce Cooling Energy
Increase Cooling Reliability
Control Modules
Collect cooling power and temperature data
*AI Engine can be deployed in cloud or on site

6. Predictive Control, Machine Learning, Analytics
11/5/2017
Predictive Control, Machine Learning, Analytics 6
Confidential

7. Predictive Control, and Machine Learning
11/5/2017
Predictive Control, and Machine Learning
Data
1.2 million sensor readings per day
85,000 control actions per day
Influence Model (for Prediction)
37,000 influences (gains)
75,000 time constants
1.1 million covariance terms
Machine Learning
Active singularity management
Passive singularity management
Fast and efficient 7
Confidential

8. Machine Learning for Extinguishing Hot Spots
11/5/2017
Machine Learning for Extinguishing Hot Spots
Unit #7
Unit #1
Either cooling unit will extinguish the hot spot 8
Confidential

9. Efficiency Comparison
11/5/2017
Efficiency Comparison
Cooling unit #1 draws 13 kW
Cooling unit #7 draws 8 kW
Both can extinguish hot spot
So start unit #7 9
Confidential

10. Predict Risk of Maintenance
11/5/2017
Predict Risk of Maintenance
Actual map at T+30 minutes
Before shutting
cooling unit off
Prediction of T+30
made at shutoff 10
Confidential

11. Cooling Asset and Capacity Management
11/5/2017
Cooling Asset and Capacity Management 11
Confidential

12. Insights from Real-World Deployments
3/1/12
Insights from Real-World Deployments
Energy
Machine learning results in non-intuitive decisions that save money
Reliability
Machine learning can help mitigate the risk of maintenance
Capacity
Re-distribute cooling assets to reduce CapEx and reduce time to revenue
People feel uncomfortable initially, but they discover that software frees them up from tactical concerns and helps them find important facts that they would not otherwise find
Vigilent Confidential

13. Cliff Federspiel
Vigilent President and CTO