Smart Wind and Solar Power \u2013 (Technology Review \u2013 April 23, 2014)<\/a><\/b>
\nBig data and artificial intelligence are producing ultra-accurate forecasts that will make it feasible to integrate much more renewable energy into the grid. Wind power is booming on the open plains of eastern Colorado where rows of towering wind turbines stretch for miles. Every few seconds, almost every one of the hundreds of turbines records the wind speed and its own power output. Every five minutes they dispatch data to high-performance computers 100 miles away at the National Center for Atmospheric Research (NCAR) in Boulder. There artificial-intelligence-based software crunches the numbers, along with data from weather satellites, weather stations, and other wind farms in the state. The result: wind power forecasts of unprecedented accuracy that are making it possible for Colorado to use far more renewable energy, at lower cost, than utilities ever thought possible. The forecasts are helping power companies deal with one of the biggest challenges of wind power: its intermittency. Using small amounts of wind power is no problem for utilities. However, a utility that wants to use a lot of wind power needs backup power to protect against a sudden loss of wind. These backup plants, which typically burn fossil fuels, are expensive and dirty. But with more accurate forecasts, utilities can cut the amount of power that needs to be held in reserve, minimizing their role. An early version of NCAR\u2019s forecasting system was released in 2009, but last year was a breakthrough year\u2014accuracy improved significantly, and the forecasts saved Xcel, Colorado\u2019s major power company, nearly as much money as they had in the three previous years combined. This year NCAR is testing a similar forecasting system for solar power.<\/p>\nTRANSPORTATION<\/b><\/p>\n
The Self-driving Car Masters City-street Driving \u2013 (Kurzweil AI \u2013 May 1, 2014)<\/a><\/b>
\nGoogle has shifted the focus of its self-driving car project onto mastering city-street driving. \u201cSince the last update, we\u2019ve logged thousands of miles on the streets of our hometown of Mountain View, Calif. A mile of city driving is much more complex than a mile of freeway driving, with hundreds of different objects moving according to different rules of the road in a small area,\u201d Google says. \u201cWe\u2019ve improved our software so it can detect hundreds of distinct objects simultaneously \u2014 pedestrians, buses, a stop sign held up by a crossing guard, or a cyclist making gestures that indicate a possible turn. A self-driving vehicle can pay attention to all of these things in a way that a human physically can\u2019t \u2014 and it never gets tired or distracted. As it turns out, what looks chaotic and random on a city street to the human eye is actually fairly predictable to a computer.\u201d Google\u2019s self-driving vehicles have now logged nearly 700,000 autonomous miles. \u201cWith every passing mile we\u2019re growing more optimistic that we\u2019re heading toward an achievable goal \u2014 a vehicle that operates fully without human intervention,\u201d Google says. (Editor\u2019s note: This technology could solve the problem of seniors who are no longer able to drive but who live in places where no public transportation exists.)<\/p>\n