I have always been amazed at the sales predictions for new things or newly improved things. Not only are they for huge quantities, but for huge market sizes. What they look like is the classical "Hype", designed to fake a demand in order to raise stock prices. The best line concerning these predictions comes from the "Dilbert" cartoon, where the pointy-haired boss is explaining how they get their sales projections, "First you must make assumptions". That sort of says it all.
Nassim Taleb refers to disruptive technologies and other unpredictable events (with either negative or positive effects) as Black Swans, in his book of the same title. He makes a very good point that only the unimportant parts of our lives are subject to Gaussian distributions and are therefore predictable. Everything important follows a power law distribution where life changing events are rare and unique, and therefore unpredictable.
Bill, instead of checking back in with IEA to see how their predictions turned out why don't you look for their past predictions? Although you already know what you'll find, if they're even available.
Predictions tend to be more viable when they are less specific and have a greater accepted margin for error. On a gross scale, many things can be extrapolated with a reasonable degree of accuracy. When you start getting into a lot of detail, it gets more into the fortune telling arena. Also, in many cases, prediction is as much about creating a self-fulfilling prophecy as it is about knowing what will happen.
I can see how simply looking at the curve of past global power consumption could lead to an estimation close enough to be used to forecast the number of power plants required. Apply the power consumption curves from industrialized nations to developing economies like China and India and you can even get a fair idea of what they will need.
The problem really comes in if you add in some disruptive technology, like viable cheap fusion. Then all bets are off. If something like that were to happen, much of the existing infrastructure would need to be replaced regardless of any predictions so it wouldn't really matter to be way off. Without that disruptive technology, though, the repercussions from not extrapolating and planning could be pretty severe.
Problem is that for some decisions one needs to have a 40 year view. A nuclear power plant will operate up to 50 years from now so somehow people need to take some assumption about the future. Same applies for many other investments in power supply.
So instead of having no idea it is better to have something to start with. This may then still be corrected over time.
Well, I have read that but sorry, we can't generalise it to every agency which makes global forecasts. True there are some factors which could nudge them to keep their part of the luch safe and assured but not every factor can be attributed to it.
But still I have to agree with Bill on this, forecasting 5-10 years is kind of ok when taken with a pinch of salt but anything beyond is mere prophecy.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Are the design challenges the same as with embedded systems, but with a little developer- and IT-skills added in? What do engineers need to know? Rick Merritt talks with two experts about the tools and best options for designing IoT devices in 2016. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.