The Algorithm Wasn’t Built for Energy. That’s Why It Works.

John Carmean

The most common question I get when presenting validation data to energy executives is some version of: how long have you been in energy?

I haven’t. And that’s the point.

The algorithm was developed in an unrelated field. When I ran it against public federal energy data, with no modifications, it detected major grid and market events months before they materialized. It also detected failures in automotive safety and housing markets. Same algorithm. Same locked parameters. Different domains. Every time.

This makes domain experts uncomfortable. If you have spent twenty years building energy models, the idea that an outsider’s algorithm can read your grid is unsettling. An understandable reaction. But the discomfort points at something important: the best predictive instruments are often the ones that were never optimized for the domain they end up serving.

Domain-specific models are powerful, but they carry a structural risk. They are tuned to the patterns their designers expected to find. When the failure mode is novel, when the cause is geopolitical instead of meteorological, or when stress converges across systems that are usually modeled independently, domain-specific tools can miss it. Not because they are bad tools. Because they were built to look for specific things.

A domain-agnostic approach has a different advantage. It does not anticipate what the system is supposed to do. It only knows what the system has been doing and whether that behavior is changing. That makes it less precise for routine operations and significantly more sensitive to the kind of stress convergence that precedes catastrophic events.

The 2022 energy crisis was not caused by one thing. It was caused by the intersection of geopolitical disruption, supply chain stress, weather patterns, storage deficits, and demand surges all compounding simultaneously. No single-domain model was designed to synthesize all those inputs. A domain-agnostic instrument can, precisely because it was never designed for any of them.

Cross-domain validation is not a marketing claim. It is the methodology. If a pattern only works in one environment, it is curve-fitting. If it works across unrelated environments with locked parameters, the method is detecting something fundamental about how complex systems behave under stress.

That distinction is worth understanding. Especially if you run something that cannot afford to fail.

John Carmean developed a drift detection methodology validated across energy, transportation, and financial domains using U.S. public domain datasets with locked parameters and no domain-specific tuning.