When it comes to understanding and predicting trends in energy use, the internet is a tough nut to crack. So say energy researchers in two recent articles that discuss the pitfalls that plague estimates of the internet’s energy and carbon impacts.
The paper describes how these errors can lead well-intentioned studies to predict massive energy growth in the information technology (IT) sector, which often doesn’t materialize. «We’re not saying the energy use of the internet isn’t a problem, or that we shouldn’t worry about it,» Masanet explained. «Rather, our main message is that we all need to get better at analyzing internet energy use and avoiding these pitfalls moving forward.»
Masanet, the Mellichamp Chair in Sustainability Science for Emerging Technologies at UCSB’s Bren School of Environmental Science & Management, has researched energy analysis of IT systems for more than 15 years. Koomey, who has studied the subject for over three decades, was for many years a staff scientist and group leader at Lawrence Berkeley National Lab, and has served as a visiting professor at Stanford University, Yale University and UC Berkeley. The article, which has no external funding source, arose out of their combined experiences and observations and was motivated by the rising public interest in internet energy use. Although the piece contains no new data or conclusions about the current energy use or environmental impacts of different technologies and sectors, it raises some important technical issues the field currently faces.
Masanet and Koomey’s work involves gathering data and building models of energy use to understand trends and make predictions. Unfortunately, IT systems are complicated and data is scarce. «The internet is a really complex system of technologies and it changes fast,» Masanet said. What’s more, in the competitive tech industry, companies often guard energy and performance data as proprietary trade secrets. «There’s a lot of engineering that goes into their operations,» he added, «and they often don’t want to give that up.»
Four fallacies
This feeds directly into the first of four major pitfalls the two researchers identified: oversimplification. Every model is a simplification of a real-world system. It has to be. But simplification becomes a pitfall when analysts overlook important aspects of the system. For example, models that underestimate improvements to data center efficiency often overestimate growth in their energy use.
Story Source: Materials provided by University of California — Santa Barbara. Original written by Harrison Tasoff. Note: Content may be edited for style and length.