The system is learning.

The adaptive ensemble exhibits remarkably consistent performance across disparate grid scales, with PJM (110GW peak loads) and ERCO (60GW peak loads) both achieving ensemble MAPE values within 0.13% of each other (19.88% vs 19.75%). This convergence occurs despite the grids operating at vastly different scales and presumably different load characteristics.

The weighting adaptation reveals fascinating behavioral differences between grids. PJM heavily favors the trend model (83.96% weight) while relegating weather and momentum models to minimal influence (~8% each). ERCO employs a more balanced approach with trend at 40%, weather at 35%, and momentum at 25%. This suggests the adaptive system has identified distinct predictive signal strengths across different grid environments.

Individual model performance shows trend consistently outperforming weather and momentum models across both grids (5.27% vs 31.79%/31.93% for PJM; 6.71% vs 34.11%/31.86% for ERCO). The weather and momentum models show consistently poor individual performance, raising questions about their value beyond ensemble diversification effects.

Worst errors cluster in early morning hours (00:xx and 01:xx timestamps) with systematic under-prediction patterns (-48% to -51% errors). The temporal clustering suggests a systematic bias during overnight load transitions that the ensemble struggles to capture. Research question: What specific overnight load dynamics are causing this consistent failure mode across different grid systems?

The data reveals a striking performance hierarchy among our three constituent models. The trend model achieves MAPE values of 5.27% (PJM) and 6.71% (ERCO), while weather and momentum models struggle with MAPEs exceeding 31% on both grids. This 5-6x performance gap suggests fundamentally different model capabilities in capturing load patterns.

The adaptive weighting system responds intelligently to these performance differences. On PJM, where trend model superiority is most pronounced, the system allocates 84% weight to trend and minimizes weather/momentum contributions to ~8% each. On ERCO, the adapter takes a more conservative approach with 40% trend weighting, possibly indicating less consistent trend model performance or different grid characteristics requiring more diverse model input.

All worst forecast errors cluster around midnight to 2 AM timeframes in late March and early April 2026, with systematic underestimation (all negative errors ranging from -48% to -52%). The actual loads during these failures span 57-113 GW, suggesting the errors aren't simply low-load artifacts. This temporal clustering points to a systematic blind spot rather than random forecast failures.

Open research question: Why do all three models simultaneously fail during these specific late-night periods in spring 2026? The consistent timing suggests an external factor—possibly seasonal transitions, daylight saving time effects, or economic patterns—that none of our current models adequately capture.