Data drives efficiency: will more computing power lead to lower energy consumption?

jet engine

Imagine standing next to one of the four massive engines of a Boeing 747. Cruising at 900km/h high above the earth, this 70m-long plane burns approximately 250 liters of kerosene per minute. That number can quickly be reduced by 1-5%. How? By using sensors on the jet’s blade to collect data, a wireless information transmission system, real-time analysis and optimization controls.

For industrial hardware companies, more information on equipment operations is an opportunity to reinvent their business. GE now has 250,000 “intelligent” machines — MR-scanners, gas turbines and jet engines — fitted with sensors, wireless technology, and controls. Some years ago, GE sold only hardware, dealing with a customer once per lifetime (if all went well, that is). Today, GE can build a continuous relationship with customers, informing them how to run their machines more efficiently, or even controlling the equipment for them.

This jump in productivity is enabled by two trends: more data and faster information processing.

First, we can cheaply gather great amounts of data. Sensors are becoming smaller and cheaper every year. In addition, the need to place additional hardware decreases – much information can be picked up from existing devices. You can measure space occupancy of a college dorm by the number of wifi signals; velocity by your smartphone’s gyroscope or air pollution using spectrometers.

Second, we can now process large amounts of data near real-time. This is enabled by the increasing computer power per dollar, aided by computational techniques like machine learning.

But these two trends don’t influence only airline companies. Nest’s smart thermostat uses motion sensors, machine learning and thermostat controls to make your house more comfortable and reduce your energy bill. These little magical devices can reduce your energy bill by up to 40%, using the trends above to their full potential.

Not long ago, the drivers for energy efficiency were material science, mechanical design and behavioral change. I believe another force is becoming a very important driver: data analysis. May energy  consumption drop because of it.

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