From smartphone assistants to image recognition
and translation, machine learning already
helps us in our everyday lives.
But it can also help us to tackle some of
the world’s most challenging physical problems
-- such as energy consumption.
Large-scale commercial and industrial systems
like data centers consume a lot of energy,
and while much has been done to stem the growth
of energy use, there remains a lot more to
do given the world’s increasing need for
computing power.
Google is taking many steps to reduce energy
consumptions .
Compared to five years ago, Google now get
around 3.5 times the computing power out of
the same amount of energy.
By applying DeepMind’s machine learning
to its own data centers, Google managed to
reduce the amount of energy it use for cooling
by up to 40 percent.
In any large scale energy-consuming environment,
this would be a huge improvement.
Given how sophisticated Google’s data centers
are already, it’s a phenomenal step forward.
The implications are significant for Google’s
data centers, given its potential to greatly
improve energy efficiency and reduce emissions
overall.
This will also help other companies who run
on Google’s cloud to improve their own energy
efficiency.
Every improvement in data center efficiency
reduces total emissions into our environment
and with technology like DeepMind’s, we
can use machine learning to consume less energy
and help address one of the biggest challenges
of all -- climate change.
One of the primary sources of energy use in
the data center environment is cooling.
Just as your laptop generates a lot of heat,
Google's data centers -- which contain servers
powering Google Search, Gmail, YouTube, etc.
-- also generate a lot of heat that must be
removed to keep the servers running.
This cooling is typically accomplished via
large industrial equipment such as pumps,
chillers and cooling towers.
However, dynamic environments like data centers
make it difficult to operate optimally for
several reasons.
To address this problem, Google began applying
machine learning two years ago to operate
their data centers more efficiently.
And over the past few months, DeepMind researchers
began working with Google’s data center
team to significantly improve the system’s
utility.
Using a system of neural networks trained
on different operating scenarios and parameters
within their data centers, Google created
a more efficient and adaptive framework to
understand data center dynamics and optimize
efficiency.
Google accomplished this by taking the historical
data that had already been collected by thousands
of sensors within the data center -- data
such as temperatures, power, pump speeds,
setpoints, etc. -- and using it to train an
ensemble of deep neural networks.
Google's machine learning system was able
to consistently achieve a 40 percent reduction
in the amount of energy used for cooling.
Because the algorithm is a general-purpose
framework to understand complex dynamics,
Google plan to apply this to other challenges
in the data center environment and beyond
in the coming months.
Possible applications of this technology include
improving power plant conversion efficiency
, reducing semiconductor manufacturing energy
and water usage, or helping manufacturing
facilities increase throughput.
