Empowering the Next-Generation Power Grid with Big Data – NEMA Currents


In 2014, VELCO began work with IBM, Vermont's distribution utilities, and other partners to develop VWAC, which integrates IBM's precise weather forecasting with Vermont's customer load data and output data from the state's renewable generators to turn this mass of data into actionable information using leading-edge analytics. Finally, VWAC provides greater visibility to potential demand response events based on demand forecasts, from the substation to the distribution service territory to the statewide level. Linking VWAC output to VELCO's energy management system will improve core grid reliability and more accurately integrate weather-dependent generation in it. Reduced uncertainty of production from behind-the-meter (BTM) solar will result in better commitment and dispatch decisions by ISO New England, reducing production costs and avoiding unnecessary supplemental commitments and fuel consumption.

PG&E leverages machine-learning and data science for asset management and DER integration


In its simplest form, the answer is we need a platform that can integrate a wide variety of data sources: not just utility-owned data (eg asset location, asset type, smart meter data), but also external data sources such as weather patterns, customer-sited solar input and so forth. Now, we're working to turn these fault current indicators into smart line sensors that enable the smart grid to communicate the location of the fault instantaneously. The GOSI project set out to demonstrate real-time data integration and visualisation for distributed energy resources, evaluate benefits and use cases of a single-interface software platform – to provide a single software interface as a tool for distribution operators and engineers/power quality end users. The project developed key data, system, and user experience learnings through integrating more than 20 data sources into a single visualisation tool allowing users to view complex data sources in ways that were not possible through current solutions.

Top #M2M Brand: @ThingsExpo #IoT #IIoT #AI #ML #DL #DX #SmartCities


With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA. Join Cloud Expo / @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 20th Cloud Expo / @ThingsExpo June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track. The upcoming 20th International @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA announces that its Call For Papers for speaking opportunities is open.



A Self-Learning Power Grid with advanced monitoring controls, centralized data processing, and advanced algorithms is the key to training and developing a Self-Learning Power Grid that will result in optimized grid operations and thus reducing costs and response times. All of this is going to be achieved with Advanced Machine Learning applications in the Power Systems Domain. Deriving meaningful analytics from disparate data sources without the right tools and technology is the challenge and this is what is being addressed by big data technologies, the application of machine learning techniques, and other learning algorithms. Now is the time to talk about integration of Modern Grid with Big Data, Machine Learning and Advance Algorithms.

ExxonMobil: Machine Learning


ExxonMobil Research and Engineering Company's Corporate Strategic Research (CSR) laboratory has an immediate opening for a full-time staff position in the area of machine learning in our Data Analytics and Optimization Section. We are seeking a talented and creative individual to conduct fundamental research in machine learning, statistics, signal processing and pattern recognition. Research will include developing methods for statistical inference on complex systems with uncertainty and missing data, developing deep neural networks for pattern recognition and optimization applications, and building models for time-series and spatiotemporal data. The candidate should have a theoretical and applied background in machine learning, signal processing or statistics.

The sunny side of the roboconomy in the Middle East


Much has been written already about the arrival of the Fourth Industrial Revolution (4IR) and the opportunity that the convergence of its new technologies offers in terms of building value into production systems and economies around the world. More than 40% of people across the Middle East and North Africa are under the age of 25, and population growth is second only to sub-Saharan Africa. This means more construction, and more opportunities to embed IoT devices into current and future builds, traffic management, energy management and other smart systems, to help the region take a leap forward. This comes down to governments adopting more progressive and inclusive frameworks that encourage demand, stimulate investment, boost enterprise development, reduce corruption and redress the imbalances of historical exclusion in some of its societies.

Why we need to create A.I.s that think in ways that we can't even imagine


The superbrain that predicts the weather will be in a different kingdom of mind from the intelligence woven into your clothes. The superbrain that predicts the weather accurately will be in a completely different kingdom of mind from the intelligence woven into your clothes. The types of artificial minds we are making now and will make in the coming century will be designed to perform specialized tasks, usually tasks beyond what we can do. Our most important thinking machines will not be machines that can think what we think faster, better, but those that think what we can't think.

This week in apps: Instagram face filters, Medium audio stories, Google Assistant on iOS and more


Reading all the news from Google I/O may have kept you too busy to keep up with this week's app news. Each week we round up the most important app news along with some of the coolest new and updated apps to help you stay in the loop with everything you need on your phone.Here's what caught our eye this week. After a months long feature by feature cloning of Snapchat, Instagram's finally gets Snapchat's most iconic feature - the lenses, or as Instagram calls them "face filters." There's also a new bottom bar for Home, Calls, Camera, People and Games.

David Carpenter: Purpose driven to the core

MIT News

When he first reported to MIT's Nuclear Reactor Laboratory (NRL) as an undergraduate in 2002, David Carpenter anticipated a challenging research opportunity. After 15 years at the NRL conducting research and earning degrees in nuclear science and engineering, Carpenter's appetite for scientific discovery remains sharp, as does his commitment to improving both the performance and safety of current and next-generation nuclear reactors. "The design is intrinsically safe because the fuel doesn't melt, and the salt can withstand high temperatures without requiring thick, pressurized containment buildings," he says. The challenges to designing this new kind of reactor involve finding optimal construction materials, since super-hot radioactive salt is highly corrosive.

How Artificial Intelligence Could Help Transform The Oil Industry


Whether its neural networks, machine learning, fuzzy logic, case-based reasoning or expert systems, AI has the potential to transform the industry. This type of technology was brought into the fortune 500 earlier this week when Intel acquired Nervana Systems, an Indian-American San Diego based startup, who was using this technology to increase operational efficiency in oil exploration. On a macro scale, deep machine learning can help to increase the awareness of macroeconomic trends to drive investment decisions in exploration and production (E&P). Although the adoption of new hard technology such as directional drilling and hydraulic fracturing brought on fracking, the O&G industry needs to continue this trend in today's low-price market to survive.