Energy Forecasting

Raw Is Not Ready: Why Reliable AI Needs Evidence Architecture Production AI has entered its awkward teenage phase. It can speak fluently, see impressively, forecast usefully, and still fail in ways that make operators quietly reach for the manual override. The problem is not simply that models are too small, not enough tokens have been burned, or someone forgot to add “think step by step” to a prompt. The deeper problem is that many AI systems are being asked to reason directly from raw inputs that have not yet been converted into the right operational form. ...

Opening — Why this matters now The fashionable version of AI strategy still sounds suspiciously like a gym membership pitch: bigger model, more parameters, more GPUs, more everything. The operational version is less glamorous and much more important: where does the computation happen, which parts of the model are actually used, how predictable is demand, and whether the system can turn those facts into lower latency, lower cost, or better decisions. ...

Humidity looks harmless on a scatter plot. Actually, in this paper, it looks worse than harmless: it appears negatively correlated with electricity demand. That is the kind of result a busy forecasting team might quietly accept. Add humidity as a feature, let the model figure it out, move on. The grid will not wait politely while everyone debates Pearl diagrams. ...