After the first paper we had five architectures benchmarked. None felt right. Transformers were wobbly at the edges. Recurrent networks were slow to adapt. State space models faded past day-ahead.
In manufacturing, when every tool has the same blind spot, you rethink the fixture.
We ran a broader search. Every credible architecture in the literature, each tested with and without weather covariates. Our first paper showed that adding weather flips the performance ranking. We tested both ways. our first paper showed that adding weather flips the performance ranking entirely.
Seven U.S. grids. One architecture per region, trained from scratch. The grids are too different for transfer learning. What works in sun-drenched California fails in wind-battered Oklahoma.
The result: 25 to 71 percent lower MAPE than ISO baselines. Across all seven grids. Not cherry-picked. Not on a favorable window.
But what convinced us to start a company was the tails. The architecture we converged on reduced the worst 5 percent of hourly errors by more than half on ERCOT. Those are the hours that trigger emergency dispatch. Those are the hours that cost real money.
By the time we submitted the paper, we had already found configurations that outperformed what we published. The paper describes the search. The product uses the result.
We are a new company. We are not pretending otherwise. Two papers. Seven grids. A team that spent a decade learning that the tail is where it matters. The benchmarks are on the site.