The power grid of the United States is one of the most complex and technical systems in operation around the world. In order to deliver consistent electricity to the entire country, a number of regional transmission organizations (RTOs) must interact and manage resources. Like with any wide-scale network-dependent system, the electric grid is vulnerable to cyberattacks from outsiders. Hackers may be looking to cause disruptions in service or may have a larger goal of affecting the supply chain of energy resources. The U.S. government and the electric and gas companies are now moving into a more technology-focused future where new sciences like artificial intelligence and machine learning can be leveraged to help secure the power grid, its infrastructure, and customers nationwide.

DeepMind claims it has trained an artificial intelligence system how to predict the energy output of Google wind farms in the U.S. The variable nature of wind makes it difficult to accurately predict how much energy a wind farm could produce in any given time period. But DeepMind says that its AI system-- a neural network trained on widely available weather forecasts and historical turbine data -- can predict wind power output 36 hours ahead of actual generation with a reasonable degree of accuracy. "Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance," a team of DeepMind researchers wrote in a blog post on Tuesday. "This is important because energy sources that can be scheduled (i.e. can deliver a set amount of electricity at a set time) are often more valuable to the grid." Google claims that DeepMind's AI system has boosted the "value" of its wind energy by roughly 20 per cent.

In collaboration with its Britain-based Artificial Intelligence (AI) subsidiary DeepMind, Google has developed a system to predict wind power output 36 hours ahead of actual generation. Google said that these type of predictions can boost the value of wind energy and can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide. "Over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged," Sims Witherspoon, Programme Manager at DeepMind and Will Fadrhonc, Carbon Free Energy Programme Lead at Google wrote in a blog post this week. "However, the variable nature of wind itself makes it an unpredictable energy source - less useful than one that can reliably deliver power at a set time," they said. In search of a solution to this problem, DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central US.

Quadcopters that autonomously spot signs of facilities wear are nothing new -- French startup Sterblue, Clobotics, General Electric spinoff Avitas Systems, and Cyberhawk employ robots to look after gas terminals, oil rigs, and other assets. A problem that remains somewhat uncracked in the drone inspection space, though, is localization -- the ability to accurately suss out a drone's location with respect to the thing it's inspecting. GPS and inertial measurement units (IMUs) provide relatively granular tracking, but more accurate data might ensure better consistency and enable drones to get safely closer to inspection targets. Toward that end, a newly published paper on the preprint server Arxiv.org "Due to harsh weather conditions, wind turbines can incur a wide range of structural damage, which can severely impact their power generation abilities," the scientists explain.

Today at Microsoft Business Forward in Paris, France we connected with hundreds of global business leaders to share our vision for Microsoft Business Applications. In addition, I had the privilege to unveil new Dynamics 365 AI and mixed reality applications, and new solutions to help businesses unlock deeper insights from data across their organizations. The new capabilities we introduce biannually in our October and April releases advance our commitment to remove barriers to innovation and operational excellence; a vision reinforced by amazing stories of companies evolving their business as usual. ExxonMobil is a great example. This week, we announced that the world's largest publicly traded international oil and gas company has deployed Dynamics 365 to help improve operations in one of the world's most important oil-producing regions.

The inadequacy of traditional forecasting model based on EEMD in practical work. For WTI, the first four IMFs decomposed by EEMD are suitable as inputs. Considering the actual demand of crude oil price forecasting, a novel model based on ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) is proposed. In practical work, the model trained by historical data will be used in later data. Then the forecasting models based on EEMD need re-execute EEMD to update decomposition results of price series after getting new data.

The AI division of Google, known as DeepMind, claims its technology will boost the value of energy generated by onshore wind farms by 20%. Google purchases 2.6GW of renewable energy a year, close to 100% of its operational needs, and has agreements with 20 different wind and solar projects which could benefit from similar DeepMind analysis. Using a neural network (computer system inspired by the structure of biological networks) that was trained using weather forecasts and historical turbine data, DeepMind engineers configured their AI to predict power output of wind turbines 36 hours ahead of time. The benefits from using the AI in the management of the windfarm are threefold; better prediction of production, better prediction of power demands, and operational cost savings. DeepMind has been experimenting by applying its AI-led analysis to onshore turbines in the United States, on projects such as the Great Western Wind project in Oklahoma.

There are already networks in place that can detect seismic activity and send an alert as soon as an earthquake is underway. But the current technology doesn't actually send the alert until all of the sensors in the network covering a given area have detected seismic waves. And it could take about a minute from the moment activity is initially detected until an alert hits the wire. A minute is a long time in an emergency. Government agencies, public works, and local utilities ideally need to alert the populace and do things like halt trains and shut off power lines to potentially mitigate damage – every second counts.

Human activity on Earth has detrimentally affected the Earth's climate, which has led to whole nations melting away, animal extinction, and potentially the disappearance of clouds. But there's a possibility that the severity of climate change could can be mitigated, if we act fast and leverage another human inventions: artificial intelligence. Google's London-based, A.I. subsidiary DeepMind announced this week its most recent accomplishment, using machine learning to help make wind energy more valuable to the power grid. The company's algorithm was able to predict how much power its wind turbines would generate 36 hours ahead of time, the company explains in a blog post. This would allow wind farms to reliably delivery exact amounts of power to meet electricity demand.

Google may have dropped the motto "don't be evil" from its corporate code of conduct, but it seems that the search giant still wants to use its superpowers for good. With that mission in mind, Google and its A.I. subsidiary DeepMind have been working on a way to increase the usefulness of green energy produced by wind farms. The problem the company has been trying to solve is that, while wind energy represents an important source of carbon-free electricity, it is fundamentally unpredictable. As a result, despite its positive points, wind power is less useful to the power grid than power sources that can reliably deliver it at set times. By using machine learning artificial intelligence to predict wind output, Google and DeepMind have trained a neural network to accurately predict wind power output 36 hours ahead of the power being generated.