Most AI failures in the energy industry don’t come from bad models.
They come from bad assumptions.
And almost every assumption that works in mainstream ML collapses the minute you apply it to a physical system powered by electrons, aging assets, weather, operators, protection schemes, and 30 years of legacy decisions welded together by SCADA.
I learned this the hard way.
In my early career, I walked into power-system projects thinking the ML part would be the challenge. I was wrong. The hard part was persuading the grid to behave long enough for the model to learn anything.
If you’ve worked in energy long enough, you know exactly what I mean.
So let’s start with a truth that makes every energy engineer nod and every AI engineer uneasy:
Energy systems are non-i.i.d. (independent and identically distributed) by design.
And until we build AI that respects that, we’ll keep repeating the same failures.
Check out the full article on Dragon Energy Journal.
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