Wind


GE's research scientists are learning to meld AI with machines

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So far, nearly 400 employees from across the company have completed GE's certification program for data analytics, and about 50 scientists have moved into digital analytics jobs of the kind Nichols has taken on. They enable GE to track wear and tear on its aircraft engines, locomotives, gas turbines, and wind turbines using sensor data instead of assumptions or estimates, making it easier to predict when they will need maintenance. What's more, if data is corrupted or missing, the company fills in the gaps with the aid of machine learning, a type of AI that lets computers learn without being explicitly programmed, says Colin Parris, GE Global Research's vice president for software research. Parris says GE pairs computer vision with deep learning, a type of AI particularly adept at recognizing patterns, and reinforcement learning, another recent advance in AI that enables machines to optimize operations, to enable cameras to find minute cracks on metal turbine blades even when they are dirty and dusty.


General Electric Builds An Ai Workforce MIT Technology Review Stage Fright Media

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So far, nearly 400 employees from across the company have completed GE's certification program for data analytics, and about 50 scientists have moved into digital analytics jobs of the kind Nichols has taken on. They enable GE to track wear and tear on its aircraft engines, locomotives, gas turbines, and wind turbines using sensor data instead of assumptions or estimates, making it easier to predict when they will need maintenance. What's more, if data is corrupted or missing, the company fills in the gaps with the aid of machine learning, a type of AI that lets computers learn without being explicitly programmed, says Colin Parris, GE Global Research's vice president for software research. Parris says GE pairs computer vision with deep learning, a type of AI particularly adept at recognizing patterns, and reinforcement learning, another recent advance in AI that enables machines to optimize operations, to enable cameras to find minute cracks on metal turbine blades even when they are dirty and dusty.


How Google is using big data to protect the environment

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During the SXSW Eco conference this week, I caught up with Google's sustainability officer, Kate Brandt, to find out more. Some of the projects involve collecting and analyzing data that enable Google and other businesses to use more sustainable materials, reduce their environmental impact and cut emissions. Another often-cited effort: the company is a big wind and solar energy investor, having signed 2.5 gigawatts worth of contracts around the world and, additionally, committed to investing 2.5bn in renewable energy, including owning stakes in power plants. At a sustainability conference in June, I attended a panel of three Google employees who recounted their effort to promote reusable menstrual cups, which help to reduce the amount of pads and tampons that end up in landfills.


[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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The ServusNet team has many years of experience in telecom network management and is building on this expertise to deliver innovative operations software products designed to reduce operating costs, improve production efficiency and increase planning accuracy. This post outlines Microsoft's collaboration with ServusNet to migrate their existing wind power forecasting model to the Azure Cloud. ServusNet currently has an on-premises solution which uses daily weather forecasts to generate the final wind power forecasts at a farm level. The easy to use user interface in Azure ML was used to quickly replicate the functionality of their existing turbine wind power forecasting algorithm.


Calculating the financial risks of renewable energy

MIT News

But MIT spinout EverVest has built a data-analytics platform that aims to give investors rapid, accurate cash-flow models and financial risk analyses for renewable-energy projects. Recently acquired by asset-management firm Ultra Capital, EverVest's platform could help boost investment in sustainable-infrastructure projects, including wind and solar power. While an electronic spreadsheet might give an average rate of return of, say, 12 percent, the EverVest's platform would show a full analysis of the quarterly performance, including the statistical uncertainty of the rate of return. By October 2015, Cardinal Wind had expanded Feldman's algorithm into a full cash-flow modelling platform that also included analyses for solar power projects.


Machine Learning at Work in the Wind Energy Domain (Channel 9)

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With the growing focus on renewable energy, there is a need to accurately forecast energy production. In this video, we explore a typical work flow when forecasting wind energy and wrap up the conversation with possible predictive maintenance use cases for the wind turbines. Although the discussion focuses on wind energy domain, this work can be easily reused with minor tweaks for other renewable energy sources.


Germany Is Using AI to Smooth the Fluctuations in Its Power Grid

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Renewable energy like solar and wind power are changing the way we generate electricity. More specifically, the problem is that renewable energy sources can never provide a constant source of power. This early warning system, called EWeLiNE, was modelled after a similar program in the U.S. EWeLiNE takes realtime data from solar panels and wind turbines around Germany and feeds it into an algorithm that calculates the renewable energy output for the next 48 hours. This algorithm uses machine learning, and the researchers compare real data with EWeLiNE predictions to refine the algorithm and improve its accuracy.


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.


[slides] Machine Learning and Cognitive Fingerprinting @ThingsExpo #IoT #ML #CognitiveComputing

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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.