Machine learning can boost the value of wind energy

Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: 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. However, the variable nature of wind itself makes it an unpredictable energy sourceless useful than one that can reliably deliver power at a set time.In search of a solution to this problem, last year, DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farmspart of Googles global fleet of renewable energy projectscollectively 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, we 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. This is important, because energy sources that can be scheduled (i.e.Read More

Horovod Adds Support for PySpark and Apache MXNet and Additional Features for Faster Training

This article was originally published on The LF Deep Learning Foundation Blog.

Horovod, a distributed deep learning framework created by Uber, makes distributed deep learning fast and easy-to-use. Horovod improves the speed, scale, and resource allocation for

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Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro Team

Over the past several years, artificial intelligence (AI) has become an integral component of many enterprise tech stacks, facilitating faster, more efficient solutions for everything from self-driving vehicles to automated messaging platforms. On the AI spectrum, deep probabilistic programming, a

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Modeling Censored Time-to-Event Data Using Pyro, an Open Source Probabilistic Programming Language

Time-to-event modeling is critical to better understanding various dimensions of the user experience. By leveraging censored time-to-event data (data involving time intervals where some of those time intervals may extend beyond when data is analyzed), companies can gain insights on

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First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL

At Uber, hundreds of data scientists, economists, AI researchers and engineers, product analysts, behavioral scientists, and other practitioners leverage scientific methods to solve challenges on our platform. From modeling and experimentation to data analysis, algorithm development, and fundamental research, the

The post First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL appeared first on Uber Engineering Blog.

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