Wind


Artificial Intelligence and Robots to Make Offshore Windfarms Safer and Cheaper

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The University of Manchester is leading a consortium to investigate advanced technologies, including robotics and artificial intelligence, for the operation and maintenance of offshore windfarms. The £5m project will investigate the use of advanced sensing, robotics, virtual reality models and artificial intelligence to reduce maintenance cost and effort. Robots and advanced sensors will be used to minimise the need for human intervention in the hazardous offshore environment. The use of robots will allow operation in difficult or hazardous environments: sub-sea to inspect cables, in high-voltage environments to inspect high voltage equipment and around the wind turbines to check their mechanical structures.


Artificial intelligence and robots to make offshore windfarms safer and cheaper

#artificialintelligence

The University of Manchester is leading a consortium to investigate advanced technologies, including robotics and artificial intelligence, for the operation and maintenance of offshore windfarms. The £5m project will investigate the use of advanced sensing, robotics, virtual reality models and artificial intelligence to reduce maintenance cost and effort. Robots and advanced sensors will be used to minimise the need for human intervention in the hazardous offshore environment. The use of robots will allow operation in difficult or hazardous environments: sub-sea to inspect cables, in high-voltage environments to inspect high voltage equipment and around the wind turbines to check their mechanical structures.


Smart Forecasts Lower the Power of Wind and Solar

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Mining these detailed forecasts to develop a more flexible and efficient electricity system could make it much cheaper to hit ambitious international goals for reducing carbon emissions, says Bryan Hannegan, director of a $135 million facility at the National Renewable Energy Laboratory (NREL) in Golden, Colorado, that uses supercomputer simulations to develop ways to scale up renewable power. No one is more aware of the challenges of integrating wind power into the grid than Dayton Jones, a power plant dispatcher for Xcel Energy. Running backup fossil-fuel plants means "throwing carbon up into the sky": "It costs money, and it's bad for the environment." The red line--the result of subtracting wind power supply (blue) from demand (black)--shows the amount of power Xcel needs to generate with its fossil-fuel plants.


SIEMENS : Oceans of Data from the World's Offshore Wind Farms 4-Traders

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Artificial intelligence is helping to monitor wind turbines in some of the world's most remote locations. The role of Artificial Intelligence Siemens Wind Power operates a Remote Diagnostics Center in Brande, Denmark where experts keep watch over nearly 10,000 Siemens wind turbines worldwide, analyzing the steady flow of data to detect minute irregularities that might indicate impending failures. Martin Bach-Andersen, a data science expert at the Diagnostics Center, says AI and machine learning are enabling turbines to learn from data and optimize their own operation. Predicting wind power output AI-based software in wind turbines not only helps predict problems but can tell wind farms how much power to produce, changing the output minute by minute.


Numenta's Brain-Inspired Software Adds Smarts to the Grid

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People in technology know there's more and more data being created, but artificial intelligence startup Numenta is tackling a slightly different problem: the speed at which data is produced. Grid operators pay EnerNoc for aggregating many of these energy reductions at buildings to maintain a steady grid frequency. One of the main advantages of the software is that it relieves the bottleneck of finding data scientists who create the models that make data useful, says Branitzky. Numenta now needs to find the applications where its software outshines traditional predictive tools and find customers willing to pay for analytics on the fly.


Germany enlists machine learning to boost renewables revolution

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Renewable power sources such as wind now provide about one-third of Germany's electricity. In June, German meteorologists, engineers and utility firms began to test whether big data and machine learning can make these power sources more grid-friendly. And on unusually sunny and windy days -- such as on 8 May, when for about 4 hours wind and solar power generated more than 90% of the electricity that Germany consumed -- they must swiftly order coal and gas-fired power stations to reduce their output lest an influx of power'congests' the grid and increases the risk of failures. Such requests, called re-dispatches, cost German customers more than €500 million (US$553 million) a year because grid operators must compensate utility firms for adjustments to their inputs.


Machine learning in wind energy

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My own first practical exposure to building a practical AI system was when I started working as a wind energy analyst. But I kept pressing on and finally found a simple machine learning system that worked perfectly for the system; it was a simple anomaly detection system that would take a couple of inputs and figure out the correlation between them and whenever one of them is out of those normal correlation, would fire up a warning sign. We were lucky enough that a lot of data cleaning were done manually our team of analysts, by sifting through years of 10 minutes wind speed data and manually flagging whenever the sensor's broken or the data "looks weird". But it did shows basic promise of machine learning; making things smart and realizes when something's wrong before its too late.


[slides] #MachineLearning and #CognitiveComputing @CloudExpo #BigData

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In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines. Join @CloudExpo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 7-9, 2016 in New York City, for three days of intense'Internet of Things' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) IoT's use in Vertical Markets. The company's internationally recognized brands include among others Cloud Expo (@CloudExpo), Big Data Expo (@BigDataExpo), DevOps Summit (@DevOpsSummit), @ThingsExpo (@ThingsExpo), Containers Expo (@ContainersExpo) and Microservices Expo (@MicroservicesE). Cloud Expo, Big Data Expo and @ThingsExpo are registered trademarks of Cloud Expo, Inc., a SYS-CON Events company.


[slides] #MachineLearning and #CognitiveComputing @CloudExpo #BigData - MeasurementMedia in Industry & Science

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Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines.


ServusNet Forecasts Wind Power Using Cortana Analytics Suite

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This post is authored by Jaya Mathew & Hong Lu, Data Scientists at Microsoft, in collaboration with the team at ServusNet.