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Tech Giant Google Using AI to Predict Wind Energy Output

This is important, because energy sources that can be scheduled, or can deliver a set amount of electricity at a set time, are often more valuable to the grid

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Google
The Google logo is seen at a start-up campus in Paris, France, Feb. 15, 2018. VOA

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.

Google, smart compose
The Google name is displayed outside the company’s office in London, Britain. VOA

These wind farms – part of Google’s global fleet of renewable energy projects – collectively generate as much electricity as is needed by a medium-sized city.

Using a neural network trained on widely available weather forecasts and historical turbine data, the researchers configured the DeepMind system to predict wind power output 36 hours ahead of actual generation.

“Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance,” Witherspoon and Fadrhonc wrote.

Also Read- Apple to Empower Over 90,000 US Girls in Coding

This is important, because energy sources that can be scheduled, or can deliver a set amount of electricity at a set time, are often more valuable to the grid.

“To date, machine learning has boosted the value of our wind energy by roughly 20 per cent, compared to the baseline scenario of no time-based commitments to the grid,” the post said. (IANS)

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Researchers Develop AI-enabled Tool to Detect Heart Attacks

It was found that compared to CAD-RADS and other scores, the ML approach better discriminated which patients would have a cardiac event from those who would not

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A commonly used drug for treating osteoporosis or bone pain may also help reduce the risk of death by cardiovascular, heart attack and stroke, according to a study.
Heart Attack risk can be decreased by drug of osteoporosis. Pixabay

Researchers have developed an Artificial Intelligence-enabled tool which uses Machine Learning (ML) algorithms that will soon play a critical role in predicting heart attacks and other cardiac issues.

The Coronary Computed Tomography Arteriography (CCTA) gives highly detailed images of the heart vessels and is a promising tool for refining risk assessment, said researchers in the study published in the journal Radiology.

While earlier tools like the Coronary Artery Disease Reporting and Data System (CAD-RADS) emphasise on stenoses or blockages and narrowing in the coronary arteries, CCTA shows more than just stenoses.

“While CAD-RADS is an important and useful development in the management of cardiac patients, its focus on stenoses may leave out important information about the arteries,” said study lead author Kevin M. Johnson, Associate Professor at the Yale University.

Heart
The study compared people aged 41-50 years and 40 or younger heart attack survivors and found that among patients who suffer a heart attack at a young age overall is 40 or younger. VOA

The ML algorithm is able to pull out patterns in the data and predict that patients with certain patterns are more likely to have an adverse event like a heart attack than patients with other patterns.

For the study, the research team compared the ML approach with CAD-RADS and other vessel scoring systems in nearly 7,000 patients. They followed the patients for an average of nine years after CCTA.

Also Read: Indians Seek Personalized Customer Experiences The Most, Says a New Study by Adobe

It was found that compared to CAD-RADS and other scores, the ML approach better discriminated which patients would have a cardiac event from those who would not.

“The risk estimate that you get from doing the Machine Learning version of the model is more accurate than the risk estimate you’re going to get if you rely on CAD-RADS,” Johnson said. (IANS)