The global energy industry is facing disruption as it transitions from fossils to renewables (and occasionally back again). Its challenges include balancing growing demand in developing nations with the need for sustainability, and predicting the effect of extreme weather conditions on supply and demand.
Against this backdrop, GE Power – whose turbines and generators supply 30 per cent of the world’s electricity – has been working on applying Big Data, machine learning and Internet of Things (IoT) technology to build an “internet of power” to replace the linear, one-way traditional model of energy delivery.
Ganesh Bell – first and current Chief Data Officer at GE Power, tells me “The biggest opportunity is that, if you think about it, the electricity industry is still following a one-hundred-year-old model which our founder, Edison, helped to proliferate.
“It’s the generation of electrons in one source which are then transmitted in a one-way linear model. That’s how it’s been for hundreds of years but that whole infrastructure is now being tested and pushed every day because of the challenges we’re talking about.”
The answer to these challenges, Bell believes, lies in taking advantage of the networked, grid-based generation and delivery infrastructures while augmenting it with the flow of data – “We think of a world where every electron will have a data bit associated with it, and we associate and track that data and optimize it, and suddenly from a linear model we have moved to a networked model.”
It makes sense in a world where everything is increasingly becoming networked and connected – take transport for example. The move towards autonomous cars which many believe will take over from manually driven cars in the near future is possible because transport networks based on data have been built by the likes of Uber and Google.ee
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The networking built throughout GE Power’s infrastructure to enable this transition to data-driven energy distribution is GE’s own Predix platform, billed as it’s “operating system for the industrial internet.”
The platform powers every part of the analytical process from the cloud repositories to “edge” analytics – algorithms running on raw sensor or machine data as close as possible to the point it is connected, for maximum speed and elimination of “noise”.
Data feeds directly into applications such as GE’s own asset performance management software which enables equipment to be monitored even if it is from a third-party manufacturer, meaning it covers every machine in a power plant, whether or not it is manufactured by GE.
Functions enabled by advanced analytics and machine learning, such as predictive maintenance and power optimization can then be applied to critical infrastructure machinery.
