Data and Electric Power: From Deterministic Machines to Probabilistic Systems in Traditional Engineering

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.