Case Study: GE Power: Big Data, Machine Learning And 'The Internet Of Energy' Case study: GE. Fowen Bes Data, Machine learnies And 'The teternet of Enery' The global energy industry is facing disruption as it transitions from fossits to renewables (and occasionally back again). Its chalienges include balancina arowing demand in developing nations with the need for sustainability and predicting the effect of extreme weather conditions on supply and demand. Hew CE Power uses Blg Data in practice GE. Power - whose turbines and generators supply 30 percent of the world s electricity - has been workina on apphina Bia Data, machine leamina. and internet of Thinas (ist) technology to buld an "intertiet of energy" lo reptace the tradifonal, linedr, one: way model of eneegy delvery, Canesth Bell, fret and cument chict dasa officer at GE Power, tells me. "if you think about it. the electricity industry is still following a cone hundred-year-old model which our founder, Edison. heiped to proliferate. It's the generacion of electrons in one source which are then transmitted in a one-way inear model ... That whole infrastructure is now being bested and pushed every day because of the challenges we're talking about." The answer to these chatienges. Bell beleves, lies in taving advantage of the nebworked, grid-based generaton and delivery intrastructures, while augrtenting it with the fiow 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 ootimize i and touddeniv. from a lineat model, we have moved to a networked model," says Bell. It certainly makes sense in a workd where everything is increasingly becoming networked and connected - from the devices in our hemes to transport networks. Functions enabled by advanced analvics and machine leaming. such as peedictive maintenance and power ectimizason, can then be aoplied to crtical infrastructure machinery, As blell lels me, We have seen results tke reducing urgianned downtime by 5 percent reducina faise doskves by 75 bercent, reducine coerations and maintenance costs by 25 percent - and these start adding up to meanengful vatue." As well as asset performance management, GE categorures ts data-powered applications into two other groups - one is operations cplimization. Which focuses on insiahts that can be acolied across a whole plact, of erferprise. And the other is business optimization - acclications desicned to improve the crofeacilfy ef eastomers. "So they can use weather data. energy market pricing data. lots of internal and ecternal data to make sure they ais capturing every opporturity for optimizing their business and being more proftable. 2 Put together, these three cafeoories of the aoclication make us the foundations of GE Power's vition for the "digtal power plant" - the firat slep lowards making the internet of enerent a gossibilir. As an example of GE. Power's need to innovate, it seems increasingly likely that tomorrow's cars will need a cobust and reliable network of enerov framsmission and charging statices tac beyond what in avalable now. It society is ever going to transition away from petroleum-poweted vehicles in meaninglul numbers, 'smart' energy distribufion is needed to make sure power is avallable to charoe our vehicles where and when its needed. The technical detaits GEs trahsition to data-driven enerov diatribution is being powered iexcuse the puni toy as own Predix platiom, billed as its "operating tystem for the industrial internet." The platlorm is behind every part of the analytical process, from the clovid teposilories to "edoe" analytics - alaorithms runing on the raw sensor of machine data as dose as possible to the point it is colected. Data leeds drecty into applications such as CE Power's cwn asset performance management sottware. Which enables equipment to be monitsed even if if thom a third-barfy mansfacturer, meaning it covers everv machine in a bowor plant. whether of not its manulactured by GE. Ldeas and insiahts vor can steal Datafcation is, along aith decentraltalion gthe move towards generating power close to ahere if will be used) and decartionization (the move away from fossil fuels), one of the three "Ds' darugting the eneroy industry. And with that data comes areat value. A recent Wordd Economic Fonum resod concluded that the power industy witt create 51.3 trilion in value over the next 10 years by rolling out loT ideas such as those put into oractice at OE Power, As Bell puts if, When we that monitoring all these assets and colecting at the data, vou unlock huce value - and that s what we re truly focused on." Read the following case study, and briefly describe: 1. A potential mission and vision statement for each company. (4 marks) 3. Whether you would choose a centralized, decentralized or COE model for the organizations structure and why. (4 marks)