**Not AI, it is machine learning a tool to support mathematical model of a data centers mechanical systems**

If you Google Image Search “artificial intelligence” you see these images.

This is not what Google’s data center group has built with an application of machine learning. When you Google Image Search “neural network” you see this.

Google’s method to improve the efficiency of its data centers optimizes for cost is a machine learning application, not as covered in the media an artificial intelligence system. Artificial Intelligence is easy for many to assume the system thinks. Google’s Machine Learning takes 19 inputs, then creates a predicted PUE with 99.6% accuracy and the settings to achieve that PUE.

Problems to be solved

- The interactions between DC Mechanical systems and various feedback loops make it difficult to accurately predict DC efficiency using traditional engineering formulas.
- Using standard formulas for predictive modeling often produces large errors because they fail to capture such complex interdependencies.
- Testing each and every feature combination to maximize efficiency would be unfeasible given time constraints, frequent fluctuations in the IT load and weather conditions, as well as the need to maintain a stable DC environment.

These problems describe the difficulty to build a mathematical model of the system.

Why Neural Networks?

To address these problems, a neural network is selected as the mathematical framework for training DC energy efficiency models. Neural networks are a class of machine learning algorithms that mimic cognitive behavior via interactions between artificial neurons [6]. They are advantageous for modeling intricate systems because neural networks do not require the user to predefine the feature interactions in the model, which assumes relationships within the data. Instead, the neural network searches for patterns and interactions between features to automatically generate a best fit model.

There are 19 different factors input that are inputs to the neural network

1. Total server IT load [kW]

2. Total Campus Core Network Room (CCNR) IT load [kW]

3. Total number of process water pumps (PWP) running

4. Mean PWP variable frequency drive (VFD) speed [%]

5. Total number of condenser water pumps (CWP) running

6. Mean CWP variable frequency drive (VFD) speed [%]

7. Total number of cooling towers running

8. Mean cooling tower leaving water temperature (LWT) setpoint [F]

9. Total number of chillers running

10. Total number of drycoolers running

11. Total number of chilled water injection pumps running

12. Mean chilled water injection pump setpoint temperature [F]

13. Mean heat exchanger approach temperature [F]

14. Outside air wet bulb (WB) temperature [F]

15. Outside air dry bulb (DB) temperature [F]

16. Outside air enthalpy [kJ/kg]

17. Outside air relative humidity (RH) [%]

18. Outdoor wind speed [mph]

19. Outdoor wind direction [deg]

There are five hidden layers with 50 nodes per layer. The hidden layers are the blue circles in the below diagram. The red circles are the 19 different inputs. The yellow circle is the output predicted PUE.

Multiple iterations are run to reduce cost. The cost function is below.

Results:

A machine learning approach leverages the plethora of existing sensor data to develop a mathematical model that understands the relationships between operational parameters and the holistic energy efficiency. This type of simulation allows operators to virtualize the DC for the purpose of identifying optimal plant configurations while reducing the uncertainty surrounding plant changes.

Note: I have used Jim Gao’s document with some small edits to create this post.