Green Blog
A better web. Better for the environment.
First solar-powered plane completes maiden round-the-world tour, setting 19 world records
7/25/16
Posted by Amy Atlas, Communications Lead for Google Green
At 4:05am local time today, an atypical plane landed on a tarmac in Abu Dhabi: Si2, a futuristic aircraft entirely powered by solar energy.
It was imagined and built by the two Swiss explorers Bertrand Piccard and André Borschberg, who founded Solar Impulse to promote the use of clean energies. They set the goal of circumnavigating the world by air, powered by the sun, with no fuel or polluting emissions. Starting in 2004, it took the team more than a decade to design and proof test this unique aircraft. Si2 took off in March 2015 for a 17-leg journey, spanning over 26,000 miles and using 11,000 kWh worth of solar energy. After 510 flying hours, Si2 has set 19 world records, according to the Fédération Aéronautique Internationale (FAI), on this historic expedition.
Google helped build and host Solar Impulse’s digital presence, and on the first day of their round-the-world journey, we jointly launched the
#FutureIsClean initiative
, a platform to encourage the world to support the adoption of necessary clean technologies.
We’re deeply committed to powering the world with clean energy. Our goal is
100% renewable power
, and so far we've committed to purchase nearly 2.5 gigawatts of renewable energy—equivalent to taking more than 1 million cars off the road—making us the largest non-utility purchaser of renewable energy in the world.
But commitment also comes through advocacy. That’s why in 2013, Google became the internet and technology partner of Solar Impulse: to raise awareness for what's possible with clean technology and renewable energy. Everybody could use the plane’s technologies on the ground to reduce our world’s energy consumption, save natural resources and improve our quality of life.
A global community formed to join the #FutureIsClean movement, following the progression of the Si2 during its travel around the world on
www.solarimpulse.com
, and tuning in for the pilot’s conversations with the Mission Control Center in Monaco (MCC). A virtual cockpit, built with the help of Google engineers and platforms, provided the telemetrics of Si2 (altitude, speed, battery level, equipment on board, etc.) and immersed children and supporters in the technical and human challenges that Solar Impulse embarked upon.
Today, we join the rest of the world in congratulating the Solar Impulse team for this outstanding accomplishment. Solar Impulse's pioneering spirit enabled them to push human boundaries and demonstrate that clean technologies can achieve goals we once thought were impossible.
DeepMind AI reduces energy used for cooling Google data centers by 40%
7/20/16
Posted by Rich Evans, Research Engineer, DeepMind and Jim Gao, Data Center Engineer, Google
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.
Reducing energy usage has been a major focus for us over the past 10 years: we have built our own
super-efficient servers
at Google, invented
more efficient ways to cool our data centers
and invested heavily in
green energy sources
, with the goal of being powered 100 percent by renewable energy. Compared to five years ago, we now get around 3.5 times the computing power out of the same amount of energy, and we continue to make many improvements each year.
Major breakthroughs, however, are few and far between -- which is why we are excited to share that by applying DeepMind’s machine learning to our own Google data centers, we’ve managed to reduce the amount of energy we 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
. While Google is only one of many data center operators in the world, many are
not
powered by renewable energy as we are. 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, our 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:
The equipment, how we operate that equipment, and the environment interact with each other in complex, nonlinear ways. Traditional formula-based engineering and human intuition often do not capture these interactions.
The system cannot adapt quickly to internal or external changes (like the weather). This is because we cannot come up with rules and heuristics for every operating scenario.
Each data center has a unique architecture and environment. A custom-tuned model for one system may not be applicable to another. Therefore, a general intelligence framework is needed to understand the data center’s interactions.
To address this problem, we began applying
machine learning
two years ago to operate our 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 our data centers, we created a more efficient and adaptive framework to understand data center dynamics and optimize efficiency.
We 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. Since our objective was to improve data center energy efficiency, we trained the neural networks on the average future PUE (Power Usage Effectiveness), which is defined as the ratio of the total building energy usage to the IT energy usage. We then trained two additional ensembles of deep neural networks to predict the future temperature and pressure of the data center over the next hour. The purpose of these predictions is to simulate the recommended actions from the PUE model, to ensure that we do not go beyond any operating constraints.
We tested our model by deploying on a live data center. The graph below shows a typical day of testing, including when we turned the machine learning recommendations on, and when we turned them off.
Google DeepMind graph showing results of machine learning test on power usage effectiveness in Google data centers
Our machine learning system was able to consistently achieve a 40 percent reduction in the amount of energy used for cooling, which equates to a 15 percent reduction in overall PUE overhead after accounting for electrical losses and other non-cooling inefficiencies. It also produced the lowest PUE the site had ever seen.
Because the algorithm is a general-purpose framework to understand complex dynamics, we 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 (getting more energy from the same unit of input), reducing semiconductor manufacturing energy and water usage, or helping manufacturing facilities increase throughput.
We are planning to roll out this system more broadly and will share how we did it in an upcoming publication, so that other data center and industrial system operators -- and ultimately the environment -- can benefit from this major step forward.
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