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Data And Electric Power From Deterministic Machines To Probabilistic Systems In Traditional Engineering Sean Patrick Murphy

  • SKU: BELL-10427882
Data And Electric Power From Deterministic Machines To Probabilistic Systems In Traditional Engineering Sean Patrick Murphy
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Data And Electric Power From Deterministic Machines To Probabilistic Systems In Traditional Engineering Sean Patrick Murphy instant download after payment.

Publisher: O’Reilly
File Extension: PDF
File size: 4.34 MB
Pages: 40
Author: Sean Patrick Murphy
ISBN: 9781491951040, 1491951044
Language: English
Year: 2016

Product desciption

Data And Electric Power From Deterministic Machines To Probabilistic Systems In Traditional Engineering Sean Patrick Murphy by Sean Patrick Murphy 9781491951040, 1491951044 instant download after payment.

Traditional engineering is built upon a world of knowledge and scientific laws, with components and systems that operate predictably. But what happens when a large number of these devices are interconnected? You get a complex system that’s no longer deterministic, but probabilistic. That’s happening today in many industries, including manufacturing, petroleum, transportation, and energy.
In this O’Reilly report, Sean Patrick Murphy, Chief Data Scientist at PingThings, describes how data science is helping electric utilities make sense of a stochastic world filled with increasing uncertainty—including fundamental changes to the energy market and random phenomena such as weather and solar activity.
Murphy also reviews several cutting-edge tools for storing and processing big data that he’s used in his work with electric utilities—tools that can help traditional engineers pursue a data-driven approach in many industries.
Topics in this report include:
- Key drivers that have changed the electric grid from a deterministic machine into probabilistic system.
- Fundamental differences that put traditional engineering and data science at odds with one another.
- Why the time is right for engineering organizations to adopt a complete data-driven approach.
- Contemporary tools that traditional engineers can use to store and process big data.
- A PingThings case study for dealing with random geomagnetic disturbances to the energy grid.

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