Artificial intelligence could be one of humanity’s most useful inventions. AI provides enhanced control, flexibility, and value throughout the project lifetime, diversifying available value streams and balancing out the effect of variable rates. © Copyright 2020 - Cambridge Innovation Institute Google recently leveraged its DeepMind artificial intelligence (AI) system to save some money on its energy bills. 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. 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 centre. Rather than using human-implemented recommendations, DeepMind’s AI system directly controls data center cooling, remaining under the expert supervision of data center operators. The TTM energy-weighted average PUE for all Google data centers is 1.11, making our data centers among the most efficient in the world. Since our objective was to improve data centre 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. All rights reserved.
Google using DeepMind AI to reduce energy consumption by 30%. Google estimates that, according to the PUE, with DeepMind’s help energy efficiency has increased by 15%. One popular method called reinforcement learning (RL) is yielding especially impressive benefits in the energy space.RL builds algorithms through a dual approach: dataset training to optimize performance and mathematical function approximation to optimize solutions. Within the data centers, energy efficiency is calculated with a standard called PUE (Power Usage Effectiveness), which measures the relationship between the energy used by the main equipment and the energy absorbed by the auxiliary equipment. DeepMind AI reduces energy used for cooling Google data centers by 40%. Given how sophisticated Google’s data centres are already, it’s a phenomenal step forward.The implications are significant for Google’s data centres, given its potential to greatly improve energy efficiency and reduce emissions overall. Large-scale commercial and industrial systems like data centres consume a lot of … - also generate a lot of heat that must be removed to keep the servers running. It is time that AI (with all its Hollywood connotations) be seen in a new light—as a substantial technology that is fast living up to its efficiency potential.For more information on the role of AI-enabled software in energy storage, see the 2Q 2019 Navigant Research report, Opinions expressed by Forbes Contributors are their own. DeepMind AI Reduces Google Data Centre Cooling Bill by 40%. From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. Automating the system enabled us to implement more granular actions at greater frequency, while making fewer mistakes.”The AI control system has found novel ways to manage cooling that have surprised even experienced staff, such as Fuenffinger. “We’re excited that our direct AI control system is operating safely and dependably, while consistently delivering energy savings.”The technology’s successful application in just one type of business suggests that data centres could be just the beginning of AI and deep learning applications in the field of energy efficiency.In the long term, there is obvious potential for the technology to be applied in other industrial settings, including within the energy sector itself – or smart city deployments – just as scientists are warning of an approaching tipping point in climate change.More, DeepMind’s adherence to safety guidelines and its deliberate limiting of the system to minimise risk are instructive, as is building in human agency as a failsafe: a sensible model for future implementations.Receive the latest IoT news and analysis in your industry, straight to your inbox.
The graph below shows a typical day of testing, including when we turned the machine learning recommendations on, and when we turned them off.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.
close up Artificial Intelligence technology brain for backgrounds.Artificial Neural Networks cpuPerhaps no technology has generated more hype in recent years than artificial intelligence (AI). It's important to note that PUE measures the power consumption of computing equipment, independent of the other systems involved with a data center. In any large scale energy-consuming environment, this would be a huge improvement. Google DeepMind has turned its attention to making intermittent wind generation more predictable.
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